Compacted Table Data Files Validation

ABSTRACT

Database replay log compaction verification includes identifying at least one replay log of a table that includes first database manipulation commands; obtaining a compacted replay log that includes second database manipulation commands that are insert commands, where an insert command includes a column and a corresponding value; replaying, to obtain a first replay result, the first database manipulation commands; replaying, to obtain a second replay result, the second database manipulation commands; and, responsive to one row of the first replay result not matching a corresponding row of the second replay result, sending a notification including a non-match. Replaying the first database manipulation commands includes identifying condition columns of the table; responsive to the condition columns not including the column, obtaining a row corresponding to the insert command, where the row includes a modified value of the corresponding value of the column; and adding the row to the first replay result.

BACKGROUND

Advances in computer storage and database technology have led to exponential growth of the amount of data being created. Businesses are overwhelmed by the volume of the data stored in their computer systems. Existing database analytic tools are inefficient, costly to utilize, and/or require substantial configuration and training.

SUMMARY

Disclosed herein are implementations of compacted table data files validation.

A first aspect is a method for database replay log compaction verification. The method includes identifying at least one replay log of a table, the at least one replay log includes first database manipulation commands; obtaining a compacted replay log of the table, the compacted replay log includes second database manipulation commands, where the compacted replay log only includes insert commands, and where an insert command of the first database manipulation commands includes a column and a corresponding value for the column; replaying, to obtain a first replay result, the first database manipulation commands; replaying, to obtain a second replay result, the second database manipulation commands; and, responsive to one row of the first replay result not matching a corresponding row of the second replay result, sending a notification including a non-match. Replaying the first database manipulation commands includes identifying condition columns of the table, where the condition columns are used in at least one condition of the first database manipulation commands; responsive to the condition columns not including the column, obtaining a row corresponding to the insert command, where the row includes a modified value of the corresponding value of the column; and adding the row to the first replay result.

A second aspect is a method for database replay log compaction verification. The method includes replaying a first replay log to generate a first replay result, where the first replay log includes commands for obtaining an in-memory database; replaying a second replay log to generate a second replay result; and comparing the first replay result and the second replay result to verify that the first replay log and the second replay log are equivalent. Replaying the first replay log includes replacing a first value of a first field included in a first command in the first replay log with a first hash value responsive to a determination that the first field is not utilized as a condition in at least one command included in the first replay log.

A third aspect is a method for replay log compaction verification. The method includes replaying, to obtain a first replay result, first database manipulation commands of at least one replay log, where the first database manipulation commands include at least one of an update command or a delete command; replaying, to obtain a second replay result, second database manipulation commands of a compacted replay log, where the compacted replay log consists of insert commands and where the compacted replay log is a compacted version of the first replay log that has been generated using a compaction process; and, responsive to a row of the first replay result not matching a corresponding row of the second replay result, sending a notification indicating a non-match corresponding to the row of the first replay result.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not to-scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity.

FIG. 1 is a block diagram of an example of a computing device.

FIG. 2 is a block diagram of an example of a computing system.

FIG. 3 is a block diagram of an example of a low-latency database analysis system.

FIG. 4 is a flowchart of an example of a technique for validating compacted replay logs.

FIG. 5 illustrates an example of replay logs and a compacted replay log resulting therefrom.

FIG. 6 illustrates a first replay result and a second replay result.

FIG. 7 is a flowchart of an example of a technique for database replay log verification.

FIG. 8 is a flowchart of an example of a technique for database replay log compaction verification.

DETAILED DESCRIPTION

Businesses and other organizations store large amounts of data, such as business records, transaction records, and the like, in data storage systems, such as relational database systems that store data as records, or rows, having values, or fields, corresponding to respective columns in tables that can be interrelated using key values. Databases structures are often normalized or otherwise organized to maximize data density and to maximize transactional data operations at the expense of increased complexity and reduced accessibility for analysis. Individual records and tables may have little or no utility without substantial correlation, interpretation, and analysis. The complexity of these data structures and the large volumes of data that can be stored therein limit the accessibility of the data and require substantial skilled human resources to code procedures and tools that allow business users to access useful data. The tools that are available for accessing these systems are limited to outputting data expressly requested by the users and lack the capability to identify and prioritize data other than the data expressly requested. Useful data, such as data aggregations, patterns, and statistical anomalies that would not be available in smaller data sets (e.g., 10,000 rows of data), and may not be apparent to human users, may be derivable using the large volume of data (e.g., millions or billions of rows) stored in complex data storage systems, such as relational database systems, and may be inaccessible due to the complexity and limitations of the data storage systems.

In a database system, such as a low-latency database analysis system described herein, different mechanisms (e.g., tools, processes, etc.) may be available for restoring at least a portion of the database (e.g., data of the database, low-latency data of a distributed in-memory database of the low-latency database analysis system) to a previous state. It may be desirable to restore the database to a previous state in cases of database system crashes; volatile memory (e.g., low-latency memory) failures where, for example, the volatile memory includes data of the in-memory database; permanent storage failures; erroneous logic (e.g., programming, etc.) that may have introduced data errors into the database; erroneous data being loaded into the database; or any other cause that may necessitate the restoration of the database to a previous state. In an example, as the low-latency memory may be volatile, the low-latency data of the in-memory database may be backed up (e.g., persisted) in one or more non-volatile storage units (such as a solid-state drive (SSD), hard disk drive (HDD), a distributed file system (DFS), and/or the like). The in-memory state of the low-latency database can be reconstituted from the persisted state.

Scheduled or manual tasks may be performed to obtain full backups of the database, which may include backing up all of the data of the database, configuration settings of the database system, replay logs (which may also be referred to as transaction logs). A full backup represents the database system at the time that the backup is obtained. A full backup may be used to reconstitute (e.g., replicate, move, restart, etc.) the database system in the same or a different environment (e.g., machine, physical server, virtual server, cluster of servers, etc.) than that of the database system. Additionally, scheduled or manual tasks may be performed to obtain snapshots of the database. A snapshot includes the data, state, and metadata of the database system created since a last snapshot creation. A snapshot may be restored to the same, but not another, environment the snapshot was created on.

The database system may maintain replay logs that include database manipulation commands (e.g., statements, directives, instructions, etc.). The database manipulation commands can include data manipulation commands, data definition commands, or both. Data manipulation commands can be used to modify existing data of the database, add (e.g., insert) new data into the database, or delete data from the database. Data definition commands can be used to create, modify, or delete database objects, such as data that describes one or more aspects of the attributes, rows, columns, tables, relationships, indices, or other aspects of the data or database.

The database system can maintain one or more replay logs: the database system can append all received manipulation commands to one replay log; the database system can maintain one or more replay logs for each table of the database; and/or the database system can maintain version chains of replay logs. Maintaining replay logs can include more, fewer, other activity/processes, or a combination thereof.

To recover to a current state of the database, a latest full backup or snapshot may be restored and the commands of the replay logs accumulated since the latest full backup or snapshot can be replayed (e.g., re-executed, re-preformed, re-applied, etc.) on the restored latest full backup or snapshot. In an example, the snapshot may itself be stored in the format of replay logs. That is, the snapshot can be one or more replay logs that include commands for recreating all the data of the database. The database system may maintain replay logs on a per-table basis.

Replay logs may be versioned such that each set of commands that are received and processed by the database system for a table can constitute a version. As such, replay logs can be stored in the form of version chains. For ease of reference a set of commands that are received by the database system for processing and are processed by the database system is referred to herein as a loading event. The database system can include data (e.g., metadata, etc.) indicating respective versions of the tables of the database. After every load event for a table of the database system, the version of the table may be incremented by 1. As such, a version x of a table can be built on top of version x−1 of the table. When a next loading event occurs, version x+1 of the table is created on top of version x using only the commands (i.e., the data of the commands) of the load event. Loading events may be processed (e.g., handled, executed, carried out, etc.) by an enterprise data interface unit of the low-latency database analysis system.

As further described herein, the in-memory database maintains low-latency data in low-latency memory. In a case that the in-memory database is restarted (such as due to a change in configuration, a hardware failure, an algorithmic change of the in-memory database, or some other reason), the data of a table can be reconstituted (e.g., reloaded, etc.) in the low-latency memory from the replay logs corresponding to the table. For example, assume that a table named “Animals” (which is defined to include the three columns ID, NAME, and SOUND) was subject to ten loading events, where each of the loading events may affect one or more rows or columns of table. As such, ten replay logs (which may be numbered or named replayLog-Animals-0 to replayLog-Animals-9) may be associated with the Animals table. To reconstitute the low-latency data for the Animals table, the version chain of the replay logs are sequentially processed from replayLog-Animals-0 to replayLog-Animals-9.

The version chain of the replay logs for a table may become too long (e.g., may include too many replay logs) such that processing all the replay logs of the version chain consumes significant amounts of computational resources (e.g., processor time, memory, clock time, etc.). Additionally, storing the replay logs may require a significant amount of storage. The storage requirements may be compounded when the replay logs are stored to a DFS, which may create multiple copies of the replay logs. However, the use of such computational resources may be wasteful at least because the replay logs may include redundant, obsolete commands, or otherwise supplanted commands. For example, a command may be redundant if it updates a field to be the same as a previously set value of that field or a group of commands may be redundant if they insert and then delete the same row(s) in the table. As a further example, a command that inserts a first value may be obsolete if that first value is later changed to a second value and removal of the command does not affect the final state of the database once the replay logs are processed.

Furthermore, replaying the commands of a replay logs may be complex (e.g., require complex algorithms, etc.) or time consuming. For example, replaying delete, update, schema update, and UPSERT commands may require complex logic. UPSERT refers to the following scenario. When a table is created, one or more columns may be designated as primary-key columns. That is, for every row in the table, the combination of the primary-key columns values is required to be unique. When a new row is inserted, if the primary-key columns values for the new row is the same as an existing row, a database (e.g., an in-memory database) may mark the old row as deleted and inserts a new row.

High computational resource utilization can degrade the performance of the database system and may cause some operations to fail due to resource exhaustion. The possibility for degraded performance may also include or require substantially increased investment in processing, memory, and storage resources and may also result in increased energy expenditures (needed to operate those increased processing, memory, and storage resources, and for network transmissions) and associated emissions that may result from the generation of that energy. Additionally, highly complex algorithms are likely to include logic errors, which may result is data corruption that in turn degrade the reliability and utility of the database system.

The database system may compact the replay logs in a version chain to reduce the computational resource utilization and/or computational complexity. For example, to enable faster data reloads from replay logs or to reduce the disk space usage of replay logs, the database system can compact the replay logs by reducing the number of commands that may need to be performed, such as by consolidating (e.g., removing, ignoring, merging, etc.) obsolete, redundant, or otherwise supplanted commands. For example, the replay logs described above may be compacted into a single file, which may be partially named, for example, Animals_0-9.compacted, signifying that the versions 0-9 of the version chain have been compacted into this single file. To reconstitute the low-latency data of the Animals table, the database system need only process (e.g., replay) the file Animals_0-10.compacted. In an example, the compacted file (i.e., compacted replay log) only includes INSERT commands. To illustrate, a command of the form INSERT (x) followed by a command of the form UPDATE (x with y) can be compacted to a single command INSERT (y) therewith eliminating the UPDATE command altogether. Thus, compaction includes writing the in-memory state of the low-latency data to a persistent store and removing long version chains by writing the data back to disk as a single version that includes only insert commands.

Compaction may be triggered (e.g., initiated, etc.) manually or automatically (such as on a schedule or based on a determination that a table is eligible for compaction). To illustrate, and without limitations, the database system may determine that a table (e.g., the Animals table) is eligible for compaction in response to determining that the in-memory size (e.g., 100 megabyte) of the table is smaller than the combined size (e.g., 300 megabytes) of the replay logs associated with the table. Other criteria or heuristics may be used to determine the eligibility of a table for compaction.

As a first illustration of compaction, assume that a loading event includes a first command “Insert into Animals the row (ID=1, NAME=‘COW’, SOUND=‘MEOW’)” and a later loading event includes a second command “delete from Animals where ID=1.” As such, performing the second command cancels the effect of the first command. The insert command followed by the delete command on the same row is effectively a no-operation (no-op) command. Therefore, it would preserve resources to omit both commands in the compacted file (i.e., compacted replay log). As a second illustration, assume that a loading event includes a third command “Insert into Animals the row (ID=1, NAME=‘COW’, SOUND=‘MEOW’)” and a later loading event includes a fourth command “update Animals set SOUND=‘MOO’ WHERE NAME=‘COW’.” As such, instead of performing the third and fourth commands, the third and fourth commands can be compacted into the single command “Insert into Animals the row (ID=1, NAME=‘COW’, SOUND=‘MOO’).” Therefore, it would preserve resources to compact the third and fourth commands into one command.

After compaction of replay logs, the replay logs may be deleted as they are no longer needed. However, data of the database (e.g., low-latency data) may have become corrupted prior to the compaction. For examples, algorithmic changes to the database system or memory corruption may result in data corruption. To illustrate using but a simple example, whereas a received command of a loading event may have been “Insert into Animals the row (ID=1, NAME=‘COW’, SOUND=‘MEOW’),” the database system may have inserted the row (ID=7, NAME=‘COW’, SOUND=‘MEOW’) in the Animals table. In another example, the compaction instructions may include erroneous logic such that the insert commands written to the compacted file may insert data that are not according to the commands in the replay logs. For example, the compaction instructions may not fully take into account corner cases e.g., certain rare conditions that may exist in certain replay logs-which may result in data errors in the compacted replay logs.

As such, during compaction corrupted data may be written into persistent storage (i.e., the compacted file) and the correct information (contained in the replay logs) may no longer be recoverable if the original non-compacted replay logs are deleted. A possible solution to mitigate this problem is to periodically obtain backups and restore data from the backups if a problem occurs. However, taking periodic backups is inefficient because backups require a significant amount of space and takes significant time to complete. Additionally, restoring from a backup may not solve the problem because it may not be possible to recover to a latest state of the data from a backup as a backup may not include the latest data. Furthermore, there may not be an easy way, if at all, to identify whether and when (e.g., prior to or before which backup) a data corruption problem actually occurred.

Therefore, to protect the integrity of the data in a database system, it is critical to validate that first data that would result from replaying the replay logs are equivalent to second data that would result from replaying the compacted replay log corresponding to the replay logs. Prior to using (such as to restore low-latency data of an in-memory database or a portion thereof) the compacted replay log (instead of the replay logs), it is necessary to validate the compacted replay log.

Implementations according to this disclosure are designed to help protect the integrity of data in a database system, such as the integrity of low-latency data of an in-memory database, by validating that replay logs and a compacted file therefrom result in the same data and protecting against data loss (such as when replay logs are deleted therewith eliminating the possibility of data recovery as described herein); can reduce the need for frequent backups or snapshots, which may consume a significant amount of storage space, therewith resulting in a reduction of storage space; can detect data corruption when results of the validation do not match therewith initiating (such as by a database administrator) an investigation of the source of, and a resolution to, the corruption.

Validation and/or compaction according to this disclosure can be performed in a manner that optimizes usage of computational resources. For example, with respect to compute resources, validation can be performed with respect to one table at a time, with respect to one shard (e.g., table region) of an in-memory database at a time, with respect to all shards or tables of an in-memory database at a time, or with respect to some other granularity, using low-priority processes, using one compute thread, or a combination thereof. As such, the performance (e.g., query performance, data analysis performance, etc.) of the database system is not degraded during validation and/or compaction. While, for ease of understanding, the disclosure is mainly described with respect to compacting and validating replay logs associated with a table, the disclosure is not so limited and compaction and validation can, for example, also be performed with respect to one or more shards of a table. Thus, the term “table,” and unless the context indicates otherwise, should be understood to encompass, for a table that is sharded, all shards of the table or one or more shards of the table.

Validating the compacted replay log can include replaying the replay logs to obtain a first replay result; replaying the compacted replay log to obtain a second replay result; and comparing the first replay result to the second replay result.

In a conventional approach, replaying the replay logs to obtain the first replay result may reproduce data of the database exactly as the data are stored in the database. Similarly, replaying the compacted replay log to obtain the second replay result, and assuming no errors or data corruption, may reproduce the data of the database exactly as the data are stored in the database. As such, the replaying of the replay logs and/or the compacted replay log would consume a significant amount of memory (permanent or volatile, whatever the case may be). However, reproducing the data exactly as stored in the database is not necessary and is, therefore, wasteful.

Contrastingly, replaying the replay logs to obtain the first replay result according to implementations of this disclosure can modify certain data to be added to first replay result and/or the second replay result to reduce the memory used by the first replay result. A modified datum (i.e., a modified value) of a datum (i.e., a value) that is in the database can be a representation of the datum that takes less space (e.g., fewer number of bits or bytes) than the datum itself.

For example, with respect to column values that are used in commands of the replay log and that are of type string, hash values of at least some of those string values can be added to the first replay result instead of the values themselves. For example, and as can be appreciated, respective fixed-sized (e.g., 8-byte) hash values of large strings may require significantly less storage space than the strings themselves. Whether to add a column (e.g., string) value itself or a hash of the column value depends on whether the column is used in a conditional (e.g., a test, etc.) in a subsequent command of the replay logs. As hashing cannot be reversed, if a column is identified as a condition column, then the string values of the column are not hashed. On the other hand, if a column is not identified as a condition column, then all values of the column can be hashed. As such, to obtain the first replay result of replaying the replay logs, conditions columns of the commands of the replay logs are first identified. When adding data (e.g. a row) to or updating data of (e.g., modifying a column value of a row) the first replay result, the values of any columns of the added or updated data that are identified to be conditions columns are stored in the data of the first replay result without modification.

As mentioned above, the compacted replay log includes only INSERT commands. As such, no command of the compacted replay log includes conditional columns. Therefore, in an example, all values of all columns of type string can be stored in the second replay result as hashed values. In an example, each row of the second replay result can be a hash value. For example, all string values of a row can be hashed and concatenated with the values of the other columns to obtain a large concatenated string, wherein the order of the columns is preserved in the concatenated string. A hash of the large string is can be obtained and written to the second replay result. Values of the second replay result can be hashed in a way that is consistent with the hashing of the first replay result so that the first replay result and the second replay result are comparable.

To compare the first replay result to the second replay result, hashes of the rows of the first replay result can be compared to respective hashes of rows of the second replay result. In an example, a hash of a row of the first replay result can be obtained by hashing string values of columns that are not already hashed, concatenating the hashed values with the values of the other columns to obtain a large concatenated string, wherein the order of the columns is preserved in the large concatenated string. A hash of the large concatenated string can then be obtained. Other ways of obtaining hashes of rows are possible, as further described herein.

While compaction is used to explain the concepts of this disclosure, the techniques described herein can be used generally to compare at least two sets of replay logs (referred to herein as first replay log(s) and second replay log(s)). The techniques described herein can be used with/for any database system operation(s) that may write in-memory data into database manipulation commands file (i.e., second replay logs) and trigger a delete of original database manipulation commands files (i.e., first replay logs). The techniques described herein can be used to determine whether a first set of commands (e.g., first replay log(s)) is or is likely to be equivalent to a second set of commands (e.g., second replay log(s)). Equivalence in this context means that the first set of command and the second set of commands, when replayed, produce the same data.

FIG. 1 is a block diagram of an example of a computing device 1000. One or more aspects of this disclosure may be implemented using the computing device 1000. The computing device 1000 includes a processor 1100, static memory 1200, low-latency memory 1300, an electronic communication unit 1400, a user interface 1500, a bus 1600, and a power source 1700. Although shown as a single unit, any one or more element of the computing device 1000 may be integrated into any number of separate physical units. For example, the low-latency memory 1300 and the processor 1100 may be integrated in a first physical unit and the user interface 1500 may be integrated in a second physical unit. Although not shown in FIG. 1 , the computing device 1000 may include other aspects, such as an enclosure or one or more sensors.

The computing device 1000 may be a stationary computing device, such as a personal computer (PC), a server, a workstation, a minicomputer, or a mainframe computer; or a mobile computing device, such as a mobile telephone, a personal digital assistant (PDA), a laptop, or a tablet PC.

The processor 1100 may include any device or combination of devices capable of manipulating or processing a signal or other information, including optical processors, quantum processors, molecular processors, or a combination thereof. The processor 1100 may be a central processing unit (CPU), such as a microprocessor, and may include one or more processing units, which may respectively include one or more processing cores. The processor 1100 may include multiple interconnected processors. For example, the multiple processors may be hardwired or networked, including wirelessly networked. In some implementations, the operations of the processor 1100 may be distributed across multiple physical devices or units that may be coupled directly or across a network. In some implementations, the processor 1100 may include a cache, or cache memory, for internal storage of operating data or instructions. The processor 1100 may include one or more special purpose processors, one or more digital signal processor (DSP), one or more microprocessors, one or more controllers, one or more microcontrollers, one or more integrated circuits, one or more an Application Specific Integrated Circuits, one or more Field Programmable Gate Array, one or more programmable logic arrays, one or more programmable logic controllers, firmware, one or more state machines, or any combination thereof.

The processor 1100 may be operatively coupled with the static memory 1200, the low-latency memory 1300, the electronic communication unit 1400, the user interface 1500, the bus 1600, the power source 1700, or any combination thereof. The processor may execute, which may include controlling, such as by sending electronic signals to, receiving electronic signals from, or both, the static memory 1200, the low-latency memory 1300, the electronic communication unit 1400, the user interface 1500, the bus 1600, the power source 1700, or any combination thereof to execute, instructions, programs, code, applications, or the like, which may include executing one or more aspects of an operating system, and which may include executing one or more instructions to perform one or more aspects described herein, alone or in combination with one or more other processors.

The static memory 1200 is coupled to the processor 1100 via the bus 1600 and may include non-volatile memory, such as a disk drive, or any form of non-volatile memory capable of persistent electronic information storage, such as in the absence of an active power supply. Although shown as a single block in FIG. 1 , the static memory 1200 may be implemented as multiple logical or physical units.

The static memory 1200 may store executable instructions or data, such as application data, an operating system, or a combination thereof, for access by the processor 1100. The executable instructions may be organized into programmable modules or algorithms, functional programs, codes, code segments, or combinations thereof to perform one or more aspects, features, or elements described herein. The application data may include, for example, user files, database catalogs, configuration information, or a combination thereof. The operating system may be, for example, a desktop or laptop operating system; an operating system for a mobile device, such as a smartphone or tablet device; or an operating system for a large device, such as a mainframe computer.

The low-latency memory 1300 is coupled to the processor 1100 via the bus 1600 and may include any storage medium with low-latency data access including, for example, DRAM modules such as DDR SDRAM, Phase-Change Memory (PCM), flash memory, or a solid-state drive. Although shown as a single block in FIG. 1 , the low-latency memory 1300 may be implemented as multiple logical or physical units. Other configurations may be used. For example, low-latency memory 1300, or a portion thereof, and processor 1100 may be combined, such as by using a system on a chip design.

The low-latency memory 1300 may store executable instructions or data, such as application data for low-latency access by the processor 1100. The executable instructions may include, for example, one or more application programs, that may be executed by the processor 1100. The executable instructions may be organized into programmable modules or algorithms, functional programs, codes, code segments, and/or combinations thereof to perform various functions described herein.

The low-latency memory 1300 may be used to store data that is analyzed or processed using the systems or methods described herein. For example, storage of some or all data in low-latency memory 1300 instead of static memory 1200 may improve the execution speed of the systems and methods described herein by permitting access to data more quickly by an order of magnitude or greater (e.g., nanoseconds instead of microseconds).

The electronic communication unit 1400 is coupled to the processor 1100 via the bus 1600. The electronic communication unit 1400 may include one or more transceivers. The electronic communication unit 1400 may, for example, provide a connection or link to a network via a network interface. The network interface may be a wired network interface, such as Ethernet, or a wireless network interface. For example, the computing device 1000 may communicate with other devices via the electronic communication unit 1400 and the network interface using one or more network protocols, such as Ethernet, Transmission Control Protocol/Internet Protocol (TCP/IP), power line communication (PLC), Wi-Fi, infrared, ultra violet (UV), visible light, fiber optic, wire line, general packet radio service (GPRS), Global System for Mobile communications (GSM), code-division multiple access (CDMA), Long-Term Evolution (LTE), or other suitable protocols.

The user interface 1500 may include any unit capable of interfacing with a human user, such as a virtual or physical keypad, a touchpad, a display, a touch display, a speaker, a microphone, a video camera, a sensor, a printer, or any combination thereof. For example, a keypad can convert physical input of force applied to a key to an electrical signal that can be interpreted by computing device 1000. In another example, a display can convert electrical signals output by computing device 1000 to light. The purpose of such devices may be to permit interaction with a human user, for example by accepting input from the human user and providing output back to the human user. The user interface 1500 may include a display; a positional input device, such as a mouse, touchpad, touchscreen, or the like; a keyboard; or any other human and machine interface device. The user interface 1500 may be coupled to the processor 1100 via the bus 1600. In some implementations, the user interface 1500 can include a display, which can be a liquid crystal display (LCD), a cathode-ray tube (CRT), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, an active matrix organic light emitting diode (AMOLED), or other suitable display. In some implementations, the user interface 1500, or a portion thereof, may be part of another computing device (not shown). For example, a physical user interface, or a portion thereof, may be omitted from the computing device 1000 and a remote or virtual interface may be used, such as via the electronic communication unit 1400.

The bus 1600 is coupled to the static memory 1200, the low-latency memory 1300, the electronic communication unit 1400, the user interface 1500, and the power source 1700. Although a single bus is shown in FIG. 1 , the bus 1600 may include multiple buses, which may be connected, such as via bridges, controllers, or adapters.

The power source 1700 provides energy to operate the computing device 1000. The power source 1700 may be a general-purpose alternating-current (AC) electric power supply, or power supply interface, such as an interface to a household power source. In some implementations, the power source 1700 may be a single use battery or a rechargeable battery to allow the computing device 1000 to operate independently of an external power distribution system. For example, the power source 1700 may include a wired power source; one or more dry cell batteries, such as nickel-cadmium (NiCad), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium-ion (Li-ion); solar cells; fuel cells; or any other device capable of powering the computing device 1000.

FIG. 2 is a block diagram of an example of a computing system 2000. As shown, the computing system 2000 includes an external data source portion 2100, an internal database analysis portion 2200, and a system interface portion 2300. The computing system 2000 may include other elements not shown in FIG. 2 , such as computer network elements.

The external data source portion 2100 may be associated with, such as controlled by, an external person, entity, or organization (second-party). The internal database analysis portion 2200 may be associated with, such as created by or controlled by, a person, entity, or organization (first-party). The system interface portion 2300 may be associated with, such as created by or controlled by, the first-party and may be accessed by the first-party, the second-party, third-parties, or a combination thereof, such as in accordance with access and authorization permissions and procedures.

The external data source portion 2100 is shown as including external database servers 2120 and external application servers 2140. The external data source portion 2100 may include other elements not shown in FIG. 2 . The external data source portion 2100 may include external computing devices, such as the computing device 1000 shown in FIG. 1 , which may be used by or accessible to the external person, entity, or organization (second-party) associated with the external data source portion 2100, including but not limited to external database servers 2120 and external application servers 2140. The external computing devices may include data regarding the operation of the external person, entity, or organization (second-party) associated with the external data source portion 2100.

The external database servers 2120 may be one or more computing devices configured to store data in a format and schema determined externally from the internal database analysis portion 2200, such as by a second-party associated with the external data source portion 2100, or a third party. For example, the external database server 2120 may use a relational database and may include a database catalog with a schema. In some embodiments, the external database server 2120 may include a non-database data storage structure, such as a text-based data structure, such as a comma separated variable structure or an extensible markup language formatted structure or file. For example, the external database servers 2120 can include data regarding the production of materials by the external person, entity, or organization (second-party) associated with the external data source portion 2100, communications between the external person, entity, or organization (second-party) associated with the external data source portion 2100 and third parties, or a combination thereof. Other data may be included. The external database may be a structured database system, such as a relational database operating in a relational database management system (RDBMS), which may be an enterprise database. In some embodiments, the external database may be an unstructured data source. The external data may include data or content, such as sales data, revenue data, profit data, tax data, shipping data, safety data, sports data, health data, weather data, or the like, or any other data, or combination of data, that may be generated by or associated with a user, an organization, or an enterprise and stored in a database system. For simplicity and clarity, data stored in or received from the external data source portion 2100 may be referred to herein as enterprise data.

The external application server 2140 may include application software, such as application software used by the external person, entity, or organization (second-party) associated with the external data source portion 2100. The external application server 2140 may include data or metadata relating to the application software.

The external database servers 2120, the external application servers 2140, or both, shown in FIG. 2 may represent logical units or devices that may be implemented on one or more physical units or devices, which may be controlled or operated by the first party, the second party, or a third party.

The external data source portion 2100, or aspects thereof, such as the external database servers 2120, the external application servers 2140, or both, may communicate with the internal database analysis portion 2200, or an aspect thereof, such as one or more of the servers 2220, 2240, 2260, and 2280, via an electronic communication medium, which may be a wired or wireless electronic communication medium. For example, the electronic communication medium may include a local area network (LAN), a wide area network (WAN), a fiber channel network, the Internet, or a combination thereof.

The internal database analysis portion 2200 is shown as including servers 2220, 2240, 2260, and 2280. The servers 2220, 2240, 2260, and 2280 may be computing devices, such as the computing device 1000 shown in FIG. 1 . Although four servers 2220, 2240, 2260, and 2280 are shown in FIG. 2 , other numbers, or cardinalities, of servers may be used. For example, the number of computing devices may be determined based on the capability of individual computing devices, the amount of data to be processed, the complexity of the data to be processed, or a combination thereof. Other metrics may be used for determining the number of computing devices.

The internal database analysis portion 2200 may store data, process data, or store and process data. The internal database analysis portion 2200 may include a distributed cluster (not expressly shown) which may include two or more of the servers 2220, 2240, 2260, and 2280. The operation of distributed cluster, such as the operation of the servers 2220, 2240, 2260, and 2280 individually, in combination, or both, may be managed by a distributed cluster manager. For example, the server 2220 may be the distributed cluster manager. In another example, the distributed cluster manager may be implemented on another computing device (not shown). The data and processing of the distributed cluster may be distributed among the servers 2220, 2240, 2260, and 2280, such as by the distributed cluster manager.

Enterprise data from the external data source portion 2100, such as from the external database server 2120, the external application server 2140, or both may be imported into the internal database analysis portion 2200. The external database server 2120, the external application server 2140, or both may be one or more computing devices and may communicate with the internal database analysis portion 2200 via electronic communication. The imported data may be distributed among, processed by, stored on, or a combination thereof, one or more of the servers 2220, 2240, 2260, and 2280. Importing the enterprise data may include importing or accessing the data structures of the enterprise data. Importing the enterprise data may include generating internal data, internal data structures, or both, based on the enterprise data. The internal data, internal data structures, or both may accurately represent and may differ from the enterprise data, the data structures of the enterprise data, or both. In some implementations, enterprise data from multiple external data sources may be imported into the internal database analysis portion 2200. For simplicity and clarity, data stored or used in the internal database analysis portion 2200 may be referred to herein as internal data. For example, the internal data, or a portion thereof, may represent, and may be distinct from, enterprise data imported into or accessed by the internal database analysis portion 2200.

The system interface portion 2300 may include one or more client devices 2320, 2340. The client devices 2320, 2340 may be computing devices, such as the computing device 1000 shown in FIG. 1 . For example, one of the client devices 2320, 2340 may be a desktop or laptop computer and the other of the client devices 2320, 2340 may be a mobile device, smartphone, or tablet. One or more of the client devices 2320, 2340 may access the internal database analysis portion 2200. For example, the internal database analysis portion 2200 may provide one or more services, application interfaces, or other electronic computer communication interfaces, such as a web site, and the client devices 2320, 2340 may access the interfaces provided by the internal database analysis portion 2200, which may include accessing the internal data stored in the internal database analysis portion 2200.

In an example, one or more of the client devices 2320, 2340 may send a message or signal indicating a request for data, which may include a request for data analysis, to the internal database analysis portion 2200. The internal database analysis portion 2200 may receive and process the request, which may include distributing the processing among one or more of the servers 2220, 2240, 2260, and 2280, may generate a response to the request, which may include generating or modifying internal data, internal data structures, or both, and may output the response to the client device 2320, 2340 that sent the request. Processing the request may include accessing one or more internal data indexes, an internal database, or a combination thereof. The client device 2320, 2340 may receive the response, including the response data or a portion thereof, and may store, output, or both, the response or a representation thereof, such as a representation of the response data, or a portion thereof, which may include presenting the representation via a user interface on a presentation device of the client device 2320, 2340, such as to a user of the client device 2320, 2340.

The system interface portion 2300, or aspects thereof, such as one or more of the client devices 2320, 2340, may communicate with the internal database analysis portion 2200, or an aspect thereof, such as one or more of the servers 2220, 2240, 2260, and 2280, via an electronic communication medium, which may be a wired or wireless electronic communication medium. For example, the electronic communication medium may include a local area network (LAN), a wide area network (WAN), a fiber channel network, the Internet, or a combination thereof.

FIG. 3 is a block diagram of an example of a low-latency database analysis system 3000. The low-latency database analysis system 3000, or aspects thereof, may be similar to the internal database analysis portion 2200 shown in FIG. 2 , except as described herein or otherwise clear from context. The low-latency database analysis system 3000, or aspects thereof, may be implemented on one or more computing devices, such as servers 2220, 2240, 2260, and 2280 shown in FIG. 2 , which may be in a clustered or distributed computing configuration.

The low-latency database analysis system 3000 may store and maintain the internal data, or a portion thereof, such as low-latency data, in a low-latency memory device, such as the low-latency memory 1300 shown in FIG. 1 , or any other type of data storage medium or combination of data storage devices with relatively fast (low-latency) data access, organized in a low-latency data structure. In some embodiments, the low-latency database analysis system 3000 may be implemented as one or more logical devices in a cloud-based configuration optimized for automatic database analysis.

As shown, the low-latency database analysis system 3000 includes a distributed cluster manager 3100, a security and governance unit 3200, a distributed in-memory database 3300, an enterprise data interface unit 3400, a distributed in-memory ontology unit 3500, a semantic interface unit 3600, a relational search unit 3700, a natural language processing unit 3710, a data utility unit 3720, an insight unit 3730, an object search unit 3800, an object utility unit 3810, a system configuration unit 3820, a user customization unit 3830, a system access interface unit 3900, a real-time collaboration unit 3910, a third-party integration unit 3920, and a persistent storage unit 3930, which may be collectively referred to as the components of the low-latency database analysis system 3000.

Although not expressly shown in FIG. 3 , one or more of the components of the low-latency database analysis system 3000 may be implemented on one or more operatively connected physical or logical computing devices, such as in a distributed cluster computing configuration, such as the internal database analysis portion 2200 shown in FIG. 2 . Although shown separately in FIG. 3 , one or more of the components of the low-latency database analysis system 3000, or respective aspects thereof, may be combined or otherwise organized.

The low-latency database analysis system 3000 may include different, fewer, or additional components not shown in FIG. 3 . The aspects or components implemented in an instance of the low-latency database analysis system 3000 may be configurable. For example, the insight unit 3730 may be omitted or disabled. One or more of the components of the low-latency database analysis system 3000 may be implemented in a manner such that aspects thereof are divided or combined into various executable modules or libraries in a manner which may differ from that described herein.

The low-latency database analysis system 3000 may implement an application programming interface (API), which may monitor, receive, or both, input signals or messages from external devices and systems, client systems, process received signals or messages, transmit corresponding signals or messages to one or more of the components of the low-latency database analysis system 3000, and output, such as transmit or send, output messages or signals to respective external devices or systems. The low-latency database analysis system 3000 may be implemented in a distributed computing configuration.

The distributed cluster manager 3100 manages the operative configuration of the low-latency database analysis system 3000. Managing the operative configuration of the low-latency database analysis system 3000 may include controlling the implementation of and distribution of processing and storage across one or more logical devices operating on one or more physical devices, such as the servers 2220, 2240, 2260, and 2280 shown in FIG. 2 . The distributed cluster manager 3100 may generate and maintain configuration data for the low-latency database analysis system 3000, such as in one or more tables, identifying the operative configuration of the low-latency database analysis system 3000. For example, the distributed cluster manager 3100 may automatically update the low-latency database analysis system configuration data in response to an operative configuration event, such as a change in availability or performance for a physical or logical unit of the low-latency database analysis system 3000. One or more of the component units of low-latency database analysis system 3000 may access the database analysis system configuration data, such as to identify intercommunication parameters or paths.

The security and governance unit 3200 may describe, implement, enforce, or a combination thereof, rules and procedures for controlling access to aspects of the low-latency database analysis system 3000, such as the internal data of the low-latency database analysis system 3000 and the features and interfaces of the low-latency database analysis system 3000. The security and governance unit 3200 may apply security at an ontological level to control or limit access to the internal data of the low-latency database analysis system 3000, such as to columns, tables, rows, or fields, which may include using row level security.

Although shown as a single unit in FIG. 3 , the distributed in-memory database 3300 may be implemented in a distributed configuration, such as distributed among the servers 2220, 2240, 2260, and 2280 shown in FIG. 2 , which may include multiple in-memory database instances. Each in-memory database instance may utilize one or more distinct resources, such as processing or low-latency memory resources, that differ from the resources utilized by the other in-memory database instances. In some embodiments, the in-memory database instances may utilize one or more shared resources, such as resources utilized by two or more in-memory database instances.

The distributed in-memory database 3300 may generate, maintain, or both, a low-latency data structure and data stored or maintained therein (low-latency data). The low-latency data may include principal data, which may represent enterprise data, such as enterprise data imported from an external enterprise data source, such as the external data source portion 2100 shown in FIG. 2 . In some implementations, the distributed in-memory database 3300 may include system internal data representing one or more aspects, features, or configurations of the low-latency database analysis system 3000. The distributed in-memory database 3300 and the low-latency data stored therein, or a portion thereof, may be accessed using commands, messages, or signals in accordance with a defined structured query language associated with the distributed in-memory database 3300.

The low-latency data, or a portion thereof, may be organized as tables in the distributed in-memory database 3300. A table may be a data structure to organize or group the data or a portion thereof, such as related or similar data. A table may have a defined structure. For example, each table may define or describe a respective set of one or more columns.

A column may define or describe the characteristics of a discrete aspect of the data in the table. For example, the definition or description of a column may include an identifier, such as a name, for the column within the table, and one or more constraints, such as a data type, for the data corresponding to the column in the table. The definition or description of a column may include other information, such as a description of the column. The data in a table may be accessible or partitionable on a per-column basis. The set of tables, including the column definitions therein, and information describing relationships between elements, such as tables and columns, of the database may be defined or described by a database schema or design. The cardinality of columns of a table, and the definition and organization of the columns, may be defined by the database schema or design. Adding, deleting, or modifying a table, a column, the definition thereof, or a relationship or constraint thereon, may be a modification of the database design, schema, model, or structure.

The low-latency data, or a portion thereof, may be stored in the database as one or more rows or records in respective tables. Each record or row of a table may include a respective field or cell corresponding to each column of the table. A field may store a discrete data value. The cardinality of rows of a table, and the values stored therein, may be variable based on the data. Adding, deleting, or modifying rows, or the data stored therein may omit modification of the database design, schema, or structure. The data stored in respective columns may be identified or defined as a measure data, attribute data, or enterprise ontology data (e.g., metadata).

Measure data, or measure values, may include quantifiable or additive numeric values, such as integer or floating-point values, which may include numeric values indicating sizes, amounts, degrees, or the like. A column defined as representing measure values may be referred to herein as a measure or fact. A measure may be a property on which quantitative operations (e.g., sum, count, average, minimum, maximum) may be performed to calculate or determine a result or output.

Attribute data, or attribute values, may include non-quantifiable values, such as text or image data, which may indicate names and descriptions, quantifiable values designated, defined, or identified as attribute data, such as numeric unit identifiers, or a combination thereof. A column defined as including attribute values may be referred to herein as an attribute or dimension. For example, attributes may include text, identifiers, timestamps, or the like.

Enterprise ontology data may include data that defines or describes one or more aspects of the database, such as data that describes one or more aspects of the attributes, measures, rows, columns, tables, relationships, or other aspects of the data or database schema. For example, a portion of the database design, model, or schema may be represented as enterprise ontology data in one or more tables in the database.

Distinctly identifiable data in the low-latency data may be referred to herein as a data portion. For example, the low-latency data stored in the distributed in-memory database 3300 may be referred to herein as a data portion, a table from the low-latency data may be referred to herein as a data portion, a column from the low-latency data may be referred to herein as a data portion, a row or record from the low-latency data may be referred to herein as a data portion, a value from the low-latency data may be referred to herein as a data portion, a relationship defined in the low-latency data may be referred to herein as a data portion, enterprise ontology data describing the low-latency data may be referred to herein as a data portion, or any other distinctly identifiable data, or combination thereof, from the low-latency data may be referred to herein as a data portion.

The distributed in-memory database 3300 may create or add one or more data portions, such as a table, may read from or access one or more data portions, may update or modify one or more data portions, may remove or delete one or more data portions, or a combination thereof. Adding, modifying, or removing data portions may include changes to the data model of the low-latency data. Changing the data model of the low-latency data may include notifying one or more other components of the low-latency database analysis system 3000, such as by sending, or otherwise making available, a message or signal indicating the change. For example, the distributed in-memory database 3300 may create or add a table to the low-latency data and may transmit or send a message or signal indicating the change to the semantic interface unit 3600.

In some implementations, a portion of the low-latency data may represent a data model of an external enterprise database and may omit the data stored in the external enterprise database, or a portion thereof. For example, prioritized data may be cached in the distributed in-memory database 3300 and the other data may be omitted from storage in the distributed in-memory database 3300, which may be stored in the external enterprise database. In some implementations, requesting data from the distributed in-memory database 3300 may include requesting the data, or a portion thereof, from the external enterprise database.

The distributed in-memory database 3300 may receive one or more messages or signals indicating respective data-queries for the low-latency data, or a portion thereof, which may include data-queries for modified, generated, or aggregated data generated based on the low-latency data, or a portion thereof. For example, the distributed in-memory database 3300 may receive a data-query from the semantic interface unit 3600, such as in accordance with a request for data. The data-queries received by the distributed in-memory database 3300 may be agnostic to the distributed configuration of the distributed in-memory database 3300. A data-query, or a portion thereof, may be expressed in accordance with the defined structured query language implemented by the distributed in-memory database 3300. In some implementations, a data-query may be included, such as stored or communicated, in a data-query data structure or container.

The distributed in-memory database 3300 may execute or perform one or more queries to generate or obtain response data responsive to the data-query based on the low-latency data. Unless expressly described, or otherwise clear from context, descriptions herein of a table in the context of performing, processing, or executing a data-query that include accessing, such as reading, writing, or otherwise using, a table, or data from a table, may refer to a table stored, or otherwise maintained, in the low-latency distributed database independently of the data-query or may refer to tabular data obtained, such as generated, in accordance with the data-query.

The distributed in-memory database 3300 may interpret, evaluate, or otherwise process a data-query to generate one or more distributed-queries, which may be expressed in accordance with the defined structured query language. For example, an in-memory database instance of the distributed in-memory database 3300 may be identified as a query coordinator. The query coordinator may generate a query plan, which may include generating one or more distributed-queries, based on the received data-query. The query plan may include query execution instructions for executing one or more queries, or one or more portions thereof, based on the received data-query by the one or more of the in-memory database instances. Generating the query plan may include optimizing the query plan. The query coordinator may distribute, or otherwise make available, the respective portions of the query plan, as query execution instructions, to the corresponding in-memory database instances.

The respective in-memory database instances may receive the corresponding query execution instructions from the query coordinator. The respective in-memory database instances may execute the corresponding query execution instructions to obtain, process, or both, data (intermediate results data) from the low-latency data. The respective in-memory database instances may output, or otherwise make available, the intermediate results data, such as to the query coordinator.

The query coordinator may execute a respective portion of query execution instructions (allocated to the query coordinator) to obtain, process, or both, data (intermediate results data) from the low-latency data. The query coordinator may receive, or otherwise access, the intermediate results data from the respective in-memory database instances. The query coordinator may combine, aggregate, or otherwise process, the intermediate results data to obtain results data.

In some embodiments, obtaining the intermediate results data by one or more of the in-memory database instances may include outputting the intermediate results data to, or obtaining intermediate results data from, one or more other in-memory database instances, in addition to, or instead of, obtaining the intermediate results data from the low-latency data.

The distributed in-memory database 3300 may output, or otherwise make available, the results data to the semantic interface unit 3600.

The enterprise data interface unit 3400 may interface with, or communicate with, an external enterprise data system. For example, the enterprise data interface unit 3400 may receive or access enterprise data from or in an external system, such as an external database. The enterprise data interface unit 3400 may import, evaluate, or otherwise process the enterprise data to populate, create, or modify data stored in the low-latency database analysis system 3000. The enterprise data interface unit 3400 may receive, or otherwise access, the enterprise data from one or more external data sources, such as the external data source portion 2100 shown in FIG. 2 , and may represent the enterprise data in the low-latency database analysis system 3000 by importing, loading, or populating the enterprise data as principal data in the distributed in-memory database 3300, such as in one or more low-latency data structures. The enterprise data interface unit 3400 may implement one or more data connectors, which may transfer data between, for example, the external data source and the distributed in-memory database 3300, which may include altering, formatting, evaluating, or manipulating the data.

The enterprise data interface unit 3400 may receive, access, or generate metadata that identifies one or more parameters or relationships for the principal data, such as based on the enterprise data, and may include the generated metadata in the low-latency data stored in the distributed in-memory database 3300. For example, the enterprise data interface unit 3400 may identify characteristics of the principal data such as, attributes, measures, values, unique identifiers, tags, links, keys, or the like, and may include metadata representing the identified characteristics in the low-latency data stored in the distributed in-memory database 3300. The characteristics of the data can be automatically determined by receiving, accessing, processing, evaluating, or interpreting the schema in which the enterprise data is stored, which may include automatically identifying links or relationships between columns, classifying columns (e.g., using column names), and analyzing or evaluating the data.

Distinctly identifiable operative data units or structures representing one or more data portions, one or more entities, users, groups, or organizations represented in the internal data, or one or more aggregations, collections, relations, analytical results, visualizations, or groupings thereof, may be represented in the low-latency database analysis system 3000 as objects. An object may include a unique identifier for the object, such as a fully qualified name. An object may include a name, such as a displayable value, for the object.

For example, an object may represent a user, a group, an entity, an organization, a privilege, a role, a table, a column, a data relationship, a worksheet, a view, a context, an answer, an insight, a pinboard, a tag, a comment, a trigger, a defined variable, a data source, an object-level security rule, a row-level security rule, or any other data capable of being distinctly identified and stored or otherwise obtained in the low-latency database analysis system 3000. An object may represent or correspond with a logical entity. Data describing an object may include data operatively or uniquely identifying data corresponding to, or represented by, the object in the low-latency database analysis system. For example, a column in a table in a database in the low-latency database analysis system may be represented in the low-latency database analysis system as an object and the data describing or defining the object may include data operatively or uniquely identifying the column.

A worksheet (worksheet object), or worksheet table, may be a logical table, or a definition thereof, which may be a collection, a sub-set (such as a subset of columns from one or more tables), or both, of data from one or more data sources, such as columns in one or more tables, such as in the distributed in-memory database 3300. A worksheet, or a definition thereof, may include one or more data organization or manipulation definitions, such as join paths or worksheet-column definitions, which may be user defined. A worksheet may be a data structure that may contain one or more rules or definitions that may define or describe how a respective tabular set of data may be obtained, which may include defining one or more sources of data, such as one or more columns from the distributed in-memory database 3300. A worksheet may be a data source. For example, a worksheet may include references to one or more data sources, such as columns in one or more tables, such as in the distributed in-memory database 3300, and a request for data referencing the worksheet may access the data from the data sources referenced in the worksheet. In some implementations, a worksheet may omit aggregations of the data from the data sources referenced in the worksheet.

An answer (answer object), or report, may be a defined, such as previously generated, request for data, such as a resolved-request. An answer may include information describing a visualization of data responsive to the request for data.

A visualization (visualization object) may be a defined representation or expression of data, such as a visual representation of the data, for presentation to a user or human observer, such as via a user interface. Although described as a visual representation, in some implementations, a visualization may include non-visual aspects, such as auditory or haptic presentation aspects. A visualization may be generated to represent a defined set of data in accordance with a defined visualization type or template (visualization template object), such as in a chart, graph, or tabular form. Example visualization types may include, and are not limited to, chloropleths, cartograms, dot distribution maps, proportional symbol maps, contour/isopleth/isarithmic maps, daysymetric map, self-organizing map, timeline, time series, connected scatter plots, Gantt charts, steam graph/theme river, arc diagrams, polar area/rose/circumplex charts, Sankey diagrams, alluvial diagrams, pie charts, histograms, tag clouds, bubble charts, bubble clouds, bar charts, radial bar charts, tree maps, scatter plots, line charts, step charts, area charts, stacked graphs, heat maps, parallel coordinates, spider charts, box and whisker plots, mosaic displays, waterfall charts, funnel charts, or radial tree maps. A visualization template may define or describe one or more visualization parameters, such as one or more color parameters. Visualization data for a visualization may include values of one or more of the visualization parameters of the corresponding visualization template.

A view (view object) may be a logical table, or a definition thereof, which may be a collection, a sub-set, or both, of data from one or more data sources, such as columns in one or more tables, such as in the distributed in-memory database 3300. For example, a view may be generated based on an answer, such as by storing the answer as a view. A view may define or describe a data aggregation. A view may be a data source. For example, a view may include references to one or more data sources, such as columns in one or more tables, such as in the distributed in-memory database 3300, which may include a definition or description of an aggregation of the data from a respective data source, and a request for data referencing the view may access the aggregated data, the data from the unaggregated data sources referenced in the worksheet, or a combination thereof. The unaggregated data from data sources referenced in the view defined or described as aggregated data in the view may be unavailable based on the view. A view may be a materialized view or an unmaterialized view. A request for data referencing a materialized view may obtain data from a set of data previously obtained (view-materialization) in accordance with the definition of the view and the request for data. A request for data referencing an unmaterialized view may obtain data from a set of data currently obtained in accordance with the definition of the view and the request for data.

A pinboard (pinboard object), or dashboard, may be a defined collection or grouping of objects, such as visualizations, answers, or insights. Pinboard data for a pinboard may include information associated with the pinboard, which may be associated with respective objects included in the pinboard.

A context (context object) may be a set or collection of data associated with a request for data or a discretely related sequence or series of requests for data or other interactions with the low-latency database analysis system 3000.

A definition may be a set of data describing the structure or organization of a data portion. For example, in the distributed in-memory database 3300, a column definition may define one or more aspects of a column in a table, such as a name of the column, a description of the column, a datatype for the column, or any other information about the column that may be represented as discrete data.

A data source object may represent a source or repository of data accessible by the low-latency database analysis system 3000. A data source object may include data indicating an electronic communication location, such as an address, of a data source, connection information, such as protocol information, authentication information, or a combination thereof, or any other information about the data source that may be represented as discrete data. For example, a data source object may represent a table in the distributed in-memory database 3300 and include data for accessing the table from the database, such as information identifying the database, information identifying a schema within the database, and information identifying the table within the schema within the database. An external data source object may represent an external data source. For example, an external data source object may include data indicating an electronic communication location, such as an address, of an external data source, connection information, such as protocol information, authentication information, or a combination thereof, or any other information about the external data source that may be represented as discrete data.

A sticker (sticker object) may be a description of a classification, category, tag, subject area, or other information that may be associated with one or more other objects such that objects associated with a sticker may be grouped, sorted, filtered, or otherwise identified based on the sticker. In the distributed in-memory database 3300 a tag may be a discrete data portion that may be associated with other data portions, such that data portions associated with a tag may be grouped, sorted, filtered, or otherwise identified based on the tag.

The distributed in-memory ontology unit 3500 generates, maintains, or both, information (ontological data) defining or describing the operative ontological structure of the objects represented in the low-latency database analysis system 3000, such as in the low-latency data stored in the distributed in-memory database 3300, which may include describing attributes, properties, states, or other information about respective objects and may include describing relationships among respective objects.

Objects may be referred to herein as primary objects, secondary objects, or tertiary objects. Other types of objects may be used.

Primary objects may include objects representing distinctly identifiable operative data units or structures representing one or more data portions in the distributed in-memory database 3300, or another data source in the low-latency database analysis system 3000. For example, primary objects may be data source objects, table objects, column objects, relationship objects, or the like. Primary objects may include worksheets, views, filters, such as row-level-security filters and table filters, variables, or the like. Primary objects may be referred to herein as data-objects or queryable-objects.

Secondary objects may be objects representing distinctly identifiable operative data units or structures representing analytical data aggregations, collections, analytical results, visualizations, or groupings thereof, such as pinboard objects, answer objects, insights, visualization objects, and the like. Secondary objects may be referred to herein as analytical-objects.

Tertiary objects may be objects representing distinctly identifiable operative data units or structures representing operational aspects of the low-latency database analysis system 3000, such as one or more entities, users, groups, or organizations represented in the internal data, such as user objects, user-group objects, role objects, sticker objects, and the like.

The distributed in-memory ontology unit 3500 may represent the ontological structure, which may include the objects therein, as a graph having nodes and edges. A node may be a representation of an object in the graph structure of the distributed in-memory ontology unit 3500. A node, representing an object, can include one or more components. The components of a node may be versioned, such as on a per-component basis. For example, a node can include a header component, a content component, or both. A header component may include information about the node. A content component may include the content of the node. An edge may represent a relationship between nodes, which may be directional.

In some implementations, the distributed in-memory ontology unit 3500 graph may include one or more nodes, edges, or both, representing one or more objects, relationships or both, corresponding to a respective internal representation of enterprise data stored in an external enterprise data storage unit, wherein a portion of the data stored in the external enterprise data storage unit represented in the distributed in-memory ontology unit 3500 graph is omitted from the distributed in-memory database 3300.

In some embodiments, the distributed in-memory ontology unit 3500 may generate, modify, or remove a portion of the ontology graph in response to one or more messages, signals, or notifications from one or more of the components of the low-latency database analysis system 3000. For example, the distributed in-memory ontology unit 3500 may generate, modify, or remove a portion of the ontology graph in response to receiving one or more messages, signals, or notifications from the distributed in-memory database 3300 indicating a change to the low-latency data structure. In another example, the distributed in-memory database 3300 may send one or more messages, signals, or notifications indicating a change to the low-latency data structure to the semantic interface unit 3600 and the semantic interface unit 3600 may send one or more messages, signals, or notifications indicating the change to the low-latency data structure to the distributed in-memory ontology unit 3500.

The distributed in-memory ontology unit 3500 may be distributed, in-memory, multi-versioned, transactional, consistent, durable, or a combination thereof. The distributed in-memory ontology unit 3500 is transactional, which may include implementing atomic concurrent, or substantially concurrent, updating of multiple objects. The distributed in-memory ontology unit 3500 is durable, which may include implementing a robust storage that prevents data loss subsequent to or as a result of the completion of an atomic operation. The distributed in-memory ontology unit 3500 is consistent, which may include performing operations associated with a request for data with reference to or using a discrete data set, which may mitigate or eliminate the risk inconsistent results.

The distributed in-memory ontology unit 3500 may generate, output, or both, one or more event notifications. For example, the distributed in-memory ontology unit 3500 may generate, output, or both, a notification, or notifications, in response to a change of the distributed in-memory ontology. The distributed in-memory ontology unit 3500 may identify a portion of the distributed in-memory ontology (graph) associated with a change of the distributed in-memory ontology, such as one or more nodes depending from a changed node, and may generate, output, or both, a notification, or notifications indicating the identified relevant portion of the distributed in-memory ontology (graph). One or more aspects of the low-latency database analysis system 3000 may cache object data and may receive the notifications from the distributed in-memory ontology unit 3500, which may reduce latency and network traffic relative to systems that omit caching object data or omit notifications relevant to changes to portions of the distributed in-memory ontology (graph).

The distributed in-memory ontology unit 3500 may implement prefetching. For example, the distributed in-memory ontology unit 3500 may predictively, such as based on determined probabilistic utility, fetch one or more nodes, such as in response to access to a related node by a component of the low-latency database analysis system 3000.

The distributed in-memory ontology unit 3500 may implement a multi-version concurrency control graph data storage unit. Each node, object, or both, may be versioned. Changes to the distributed in-memory ontology may be reversible. For example, the distributed in-memory ontology may have a first state prior to a change to the distributed in-memory ontology, the distributed in-memory ontology may have a second state subsequent to the change, and the state of the distributed in-memory ontology may be reverted to the first state subsequent to the change, such as in response to the identification of an error or failure associated with the second state.

In some implementations, reverting a node, or a set of nodes, may omit reverting one or more other nodes. In some implementations, the distributed in-memory ontology unit 3500 may maintain a change log indicating a sequential record of changes to the distributed in-memory ontology (graph), such that a change to a node or a set of nodes may be reverted and one or more other changes subsequent to the reverted change may be reverted for consistency.

The distributed in-memory ontology unit 3500 may implement optimistic locking to reduce lock contention times. The use of optimistic locking permits improved throughput of data through the distributed in-memory ontology unit 3500.

The semantic interface unit 3600 may implement procedures and functions to provide a semantic interface between the distributed in-memory database 3300 and one or more of the other components of the low-latency database analysis system 3000.

The semantic interface unit 3600 may implement ontological data management, data-query generation, authentication and access control, object statistical data collection, or a combination thereof.

Ontological data management may include object lifecycle management, object data persistence, ontological modifications, or the like. Object lifecycle management may include creating one or more objects, reading or otherwise accessing one or more objects, updating or modifying one or more objects, deleting or removing one or more objects, or a combination thereof. For example, the semantic interface unit 3600 may interface or communicate with the distributed in-memory ontology unit 3500, which may store the ontological data, object data, or both, to perform object lifecycle management, object data persistence, ontological modifications, or the like.

For example, the semantic interface unit 3600 may receive, or otherwise access, a message, signal, or notification, such as from the distributed in-memory database 3300, indicating the creation or addition of a data portion, such as a table, in the low-latency data stored in the distributed in-memory database 3300, and the semantic interface unit 3600 may communicate with the distributed in-memory ontology unit 3500 to create an object in the ontology representing the added data portion. The semantic interface unit 3600 may transmit, send, or otherwise make available, a notification, message, or signal to the relational search unit 3700 indicating that the ontology has changed.

The semantic interface unit 3600 may receive, or otherwise access, a request message or signal, such as from the relational search unit 3700, indicating a request for information describing changes to the ontology (ontological updates request). The semantic interface unit 3600 may generate and send, or otherwise make available, a response message or signal to the relational search unit 3700 indicating the changes to the ontology (ontological updates response). The semantic interface unit 3600 may identify one or more data portions for indexing based on the changes to the ontology. For example, the changes to the ontology may include adding a table to the ontology, the table including multiple rows, and the semantic interface unit 3600 may identify each row as a data portion for indexing. The semantic interface unit 3600 may include information describing the ontological changes in the ontological updates response. The semantic interface unit 3600 may include one or more data-query definitions, such as data-query definitions for indexing data-queries, for each data portion identified for indexing in the ontological updates response. For example, the data-query definitions may include a sampling data-query, which may be used to query the distributed in-memory database 3300 for sample data from the added data portion, an indexing data-query, which may be used to query the distributed in-memory database 3300 for data from the added data portion, or both.

The semantic interface unit 3600 may receive, or otherwise access, internal signals or messages including data expressing a usage intent, such as data indicating requests to access or modify the low-latency data stored in the distributed in-memory database 3300 (e.g., a request for data). The request to access or modify the low-latency data received by the semantic interface unit 3600 may include a resolved-request. The resolved-request, which may be database and visualization agnostic, may be expressed or communicated as an ordered sequence of tokens, which may represent semantic data. For example, the relational search unit 3700 may tokenize, identify semantics, or both, based on input data, such as input data representing user input, to generate the resolved-request. The resolved-request may include an ordered sequence of tokens that represent the request for data corresponding to the input data, and may transmit, send, or otherwise make accessible, the resolved-request to the semantic interface unit 3600. The semantic interface unit 3600 may process or respond to a received resolved-request.

The semantic interface unit 3600 may process or transform the received resolved-request, which may be, at least in part, incompatible with the distributed in-memory database 3300, to generate one or more corresponding data-queries that are compatible with the distributed in-memory database 3300, which may include generating a proto-query representing the resolved-request, generating a pseudo-query representing the proto-query, and generating the data-query representing the pseudo-query.

The semantic interface unit 3600 may generate a proto-query based on the resolved-request. A proto-query, which may be database agnostic, may be structured or formatted in a form, language, or protocol that differs from the defined structured query language of the distributed in-memory database 3300. Generating the proto-query may include identifying visualization identification data, such as an indication of a type of visualization, associated with the request for data, and generating the proto-query based on the resolved-request and the visualization identification data.

The semantic interface unit 3600 may transform the proto-query to generate a pseudo-query. The pseudo-query, which may be database agnostic, may be structured or formatted in a form, language, or protocol that differs from the defined structured query language of the distributed in-memory database 3300. Generating a pseudo-query may include applying a defined transformation, or an ordered sequence of transformations. Generating a pseudo-query may include incorporating row-level security filters in the pseudo-query.

The semantic interface unit 3600 may generate a data-query based on the pseudo-query, such as by serializing the pseudo-query. The data-query, or a portion thereof, may be structured or formatted using the defined structured query language of the distributed in-memory database 3300. In some implementations, a data-query may be structured or formatted using a defined structured query language of another database, which may differ from the defined structured query language of the distributed in-memory database 3300. Generating the data-query may include using one or more defined rules for expressing respective the structure and content of a pseudo-query in the respective defined structured query language.

The semantic interface unit 3600 may communicate, or issue, the data-query to the distributed in-memory database 3300. In some implementations, processing or responding to a resolved-request may include generating and issuing multiple data-queries to the distributed in-memory database 3300.

The semantic interface unit 3600 may receive results data from the distributed in-memory database 3300 responsive to one or more resolved-requests. The semantic interface unit 3600 may process, format, or transform the results data to obtain visualization data. For example, the semantic interface unit 3600 may identify a visualization for representing or presenting the results data, or a portion thereof, such as based on the results data or a portion thereof. For example, the semantic interface unit 3600 may identifying a bar chart visualization for results data including one measure and attribute.

Although not shown separately in FIG. 3 , the semantic interface unit 3600 may include a data visualization unit. In some embodiments, the data visualization unit may be a distinct unit, separate from the semantic interface unit 3600. In some embodiments, the data visualization unit may be included in the system access interface unit 3900. The data visualization unit, the system access interface unit 3900, or a combination thereof, may generate a user interface, or one or more portions thereof. For example, data visualization unit, the system access interface unit 3900, or a combination thereof, may obtain the results data, such as the visualization data, and may generate user interface elements (visualizations) representing the results data.

The semantic interface unit 3600 may implement object-level security, row-level security, or a combination thereof. Object level security may include security associated with an object, such as a table, a column, a worksheet, an answer, or a pinboard. Row-level security may include user-based or group-based access control of rows of data in the low-latency data, the indexes, or both. The semantic interface unit 3600 may implement on or more authentication procedures, access control procedures, or a combination thereof.

The semantic interface unit 3600 may implement one or more user-data integration features. For example, the semantic interface unit 3600 may generate and output a user interface, or a portion thereof, for inputting, uploading, or importing user data, may receive user data, and may import the user data. For example, the user data may be enterprise data.

The semantic interface unit 3600 may implement object statistical data collection. Object statistical data may include, for respective objects, temporal access information, access frequency information, access recency information, access requester information, or the like. For example, the semantic interface unit 3600 may obtain object statistical data as described with respect to the data utility unit 3720, the object utility unit 3810, or both. The semantic interface unit 3600 may send, transmit, or otherwise make available, the object statistical data for data-objects to the data utility unit 3720. The semantic interface unit 3600 may send, transmit, or otherwise make available, the object statistical data for analytical-objects to the object utility unit 3810.

The semantic interface unit 3600 may implement or expose one or more services or application programming interfaces. For example, the semantic interface unit 3600 may implement one or more services for access by the system access interface unit 3900. In some implementations, one or more services or application programming interfaces may be exposed to one or more external devices or systems.

The semantic interface unit 3600 may generate and transmit, send, or otherwise communicate, one or more external communications, such as e-mail messages, such as periodically, in response to one or more events, or both. For example, the semantic interface unit 3600 may generate and transmit, send, or otherwise communicate, one or more external communications including a portable representation, such as a portable document format representation of one or more pinboards in accordance with a defined schedule, period, or interval. In another example, the semantic interface unit 3600 may generate and transmit, send, or otherwise communicate, one or more external communications in response to input data indicating an express request for a communication. In another example, the semantic interface unit 3600 may generate and transmit, send, or otherwise communicate, one or more external communications in response to one or more defined events, such as the expiration of a recency of access period for a user.

Although shown as a single unit in FIG. 3 , the relational search unit 3700 may be implemented in a distributed configuration, which may include a primary relational search unit instance and one or more secondary relational search unit instances.

The relational search unit 3700 may generate, maintain, operate, or a combination thereof, one or more indexes, such as one or more of an ontological index, a constituent data index, a control-word index, a numeral index, or a constant index, based on the low-latency data stored in the distributed in-memory database 3300, the low-latency database analysis system 3000, or both. An index may be a defined data structure, or combination of data structures, for storing tokens, terms, or string keys, representing a set of data from one or more defined data sources in a form optimized for searching. For example, an index may be a collection of index shards. In some implementations, an index may be segmented into index segments and the index segments may be sharded into index shards. In some implementations, an index may be partitioned into index partitions, the index partitions may be segmented into index segments and the index segments may be sharded into index shards.

Generating, or building, an index may be performed to create or populate a previously unavailable index, which may be referred to as indexing the corresponding data, and may include regenerating, rebuilding, or reindexing to update or modify a previously available index, such as in response to a change in the indexed data (constituent data).

The ontological index may be an index of data (ontological data) describing the ontological structure or schema of the low-latency database analysis system 3000, the low-latency data stored in the distributed in-memory database 3300, or a combination thereof. For example, the ontological index may include data representing the table and column structure of the distributed in-memory database 3300. The relational search unit 3700 may generate, maintain, or both, the ontological index by communicating with, such as requesting ontological data from, the distributed in-memory ontology unit 3500, the semantic interface unit 3600, or both. Each record in the ontological index may correspond to a respective ontological token, such as a token that identifies a column by name.

The control-word index may be an index of a defined set of control-word tokens. A control-word token may be a character, a symbol, a word, or a defined ordered sequence of characters or symbols, that is identified in one or more grammars of the low-latency database analysis system 3000 as having one or more defined grammatical functions, which may be contextual. For example, the control-word index may include the control-word token “sum”, which may be identified in one or more grammars of the low-latency database analysis system 3000 as indicating an additive aggregation. In another example, the control-word index may include the control-word token “top”, which may be identified in one or more grammars of the low-latency database analysis system 3000 as indicating a maximal value from an ordered set. In another example, the control-word index may include operator tokens, such as the equality operator token (“=”). The constant index may be an index of constant tokens such as “100” or “true”. The numeral index may be an index of number word tokens (or named numbers), such as number word tokens for the positive integers between zero and one million, inclusive. For example, “one hundred and twenty eight”.

A token may be a word, phrase, character, sequence of characters, symbol, combination of symbols, or the like. A token may represent a data portion in the low-latency data stored in the low-latency data structure. For example, the relational search unit 3700 may automatically generate respective tokens representing the attributes, the measures, the tables, the columns, the values, unique identifiers, tags, links, keys, or any other data portion, or combination of data portions, or a portion thereof. The relational search unit 3700 may classify the tokens, which may include storing token classification data in association with the tokens. For example, a token may be classified as an attribute token, a measure token, a value token, or the like.

The constituent data index may be an index of the constituent data values stored in the low-latency database analysis system 3000, such as in the distributed in-memory database 3300. The relational search unit 3700 may generate, maintain, or both, the constituent data index by communicating with, such as requesting data from, the distributed in-memory database 3300. For example, the relational search unit 3700 may send, or otherwise communicate, a message or signal to the distributed in-memory database 3300 indicating a request to perform an indexing data-query, the relational search unit 3700 may receive response data from the distributed in-memory database 3300 in response to the requested indexing data-query, and the relational search unit 3700 may generate the constituent data index, or a portion thereof, based on the response data. For example, the constituent data index may index data-objects.

An index shard may be used for token searching, such as exact match searching, prefix match searching, substring match searching, or suffix match searching. Exact match searching may include identifying tokens in the index shard that matches a defined target value. Prefix match searching may include identifying tokens in the index shard that include a prefix, or begin with a value, such as a character or string, that matches a defined target value. Substring match searching may include identifying tokens in the index shard that include a value, such as a character or string, that matches a defined target value. Suffix match searching may include identifying tokens in the index shard that include a suffix, or end with a value, such as a character or string, that matches a defined target value. In some implementations, an index shard may include multiple distinct index data structures. For example, an index shard may include a first index data structure optimized for exact match searching, prefix match searching, and suffix match searching, and a second index data structure optimized for substring match searching. Traversing, or otherwise accessing, managing, or using, an index may include identifying one or more of the index shards of the index and traversing the respective index shards. In some implementations, one or more indexes, or index shards, may be distributed, such as replicated on multiple relational search unit instances. For example, the ontological index may be replicated on each relational search unit instance.

The relational search unit 3700 may receive a request for data from the low-latency database analysis system 3000. For example, the relational search unit 3700 may receive data expressing a usage intent indicating the request for data in response to input, such as user input, obtained via a user interface, such as a user interface generated, or partially generated, by the system access interface unit 3900, which may be a user interface operated on an external device, such as one of the client devices 2320, 2340 shown in FIG. 2 . In some implementations, the relational search unit 3700 may receive the data expressing the usage intent from the system access interface unit 3900 or from the semantic interface unit 3600. For example, the relational search unit 3700 may receive or access the data expressing the usage intent in a request for data message or signal.

The relational search unit 3700 may process, parse, identify semantics, tokenize, or a combination thereof, the request for data to generate a resolved-request, which may include identifying a database and visualization agnostic ordered sequence of tokens based on the data expressing the usage intent. The data expressing the usage intent, or request for data, may include request data, such as resolved-request data, unresolved request data, or a combination of resolved-request data and unresolved request data. The relational search unit 3700 may identify the resolved-request data. The relational search unit 3700 may identify the unresolved request data and may tokenize the unresolved request data.

Resolved-request data may be request data identified in the data expressing the usage intent as resolved-request data. Each resolved-request data portion may correspond with a respective token in the low-latency database analysis system 3000. The data expressing the usage intent may include information identifying one or more portions of the request data as resolved-request data.

Unresolved request data may be request data identified in the data expressing the usage intent as unresolved request data, or request data for which the data expressing the usage intent omits information identifying the request data as resolved-request data. Unresolved request data may include text or string data, which may include a character, sequence of characters, symbol, combination of symbols, word, sequence of words, phrase, or the like, for which information, such as tokenization binding data, identifying the text or string data as resolved-request data is absent or omitted from the request data. The data expressing the usage intent may include information identifying one or more portions of the request data as unresolved request data. The data expressing the usage intent may omit information identifying whether one or more portions of the request data are resolved-request data. The relational search unit 3700 may identify one or more portions of the request data for which the data expressing the usage intent omits information identifying whether the one or more portions of the request data are resolved-request data as unresolved request data.

For example, the data expressing the usage intent may include a request string and one or more indications that one or more portions of the request string are resolved-request data. One or more portions of the request string that are not identified as resolved-request data in the data expressing the usage intent may be identified as unresolved request data. For example, the data expressing the usage intent may include the request string “example text”; the data expressing the usage intent may include information indicating that the first portion of the request string, “example”, is resolved-request data; and the data expressing the usage intent may omit information indicating that the second portion of the request string, “text”, is resolved-request data.

The information identifying one or more portions of the request data as resolved-request data may include tokenization binding data indicating a previously identified token corresponding to the respective portion of the request data. The tokenization binding data corresponding to a respective token may include, for example, one or more of a column identifier indicating a column corresponding to the respective token, a data type identifier corresponding to the respective token, a table identifier indicating a table corresponding to the respective token, an indication of an aggregation corresponding to the respective token, or an indication of a join path associated with the respective token. Other tokenization binding data may be used. In some implementations, the data expressing the usage intent may omit the tokenization binding data and may include an identifier that identifies the tokenization binding data.

The relational search unit 3700 may implement or access one or more grammar-specific tokenizers, such as a tokenizer for a defined data-analytics grammar or a tokenizer for a natural-language grammar. For example, the relational search unit 3700 may implement one or more of a formula tokenizer, a row-level-security tokenizer, a data-analytics tokenizer, or a natural language tokenizer. Other tokenizers may be used. In some implementations, the relational search unit 3700 may implement one or more of the grammar-specific tokenizers, or a portion thereof, by accessing another component of the low-latency database analysis system 3000 that implements the respective grammar-specific tokenizer, or a portion thereof. For example, the natural language processing unit 3710 may implement the natural language tokenizer and the relational search unit 3700 may access the natural language processing unit 3710 to implement natural language tokenization.

A tokenizer, such as the data-analytics tokenizer, may parse text or string data (request string), such as string data included in a data expressing the usage intent, in a defined read order, such as from left to right, such as on a character-by-character or symbol-by-symbol basis. For example, a request string may include a single character, symbol, or letter, and tokenization may include identifying one or more tokens matching, or partially matching, the input character.

Tokenization may include parsing the request string to identify one or more words or phrases. For example, the request string may include a sequence of characters, symbols, or letters, and tokenization may include parsing the sequence of characters in a defined order, such as from left to right, to identify distinct words or terms and identifying one or more tokens matching the respective words. In some implementations, word or phrase parsing may be based on one or more of a set of defined delimiters, such as a whitespace character, a punctuation character, or a mathematical operator.

The relational search unit 3700 may traverse one or more of the indexes to identify one or more tokens corresponding to a character, word, or phrase identified in request string. Tokenization may include identifying multiple candidate tokens matching a character, word, or phrase identified in request string. Candidate tokens may be ranked or ordered, such as based on probabilistic utility.

Tokenization may include match-length maximization. Match-length maximization may include ranking or ordering candidate matching tokens in descending magnitude order. For example, the longest candidate token, having the largest cardinality of characters or symbols, matching the request string, or a portion thereof, may be the highest ranked candidate token. For example, the request string may include a sequence of words or a semantic phrase, and tokenization may include identifying one or more tokens matching the input semantic phrase. In another example, the request string may include a sequence of phrases, and tokenization may include identifying one or more tokens matching the input word sequence. In some implementations, tokenization may include identifying the highest ranked candidate token for a portion of the request string as a resolved token for the portion of the request string.

The relational search unit 3700 may implement one or more finite state machines. For example, tokenization may include using one or more finite state machines. A finite state machine may model or represent a defined set of states and a defined set of transitions between the states. A state may represent a condition of the system represented by the finite state machine at a defined temporal point. A finite state machine may transition from a state (current state) to a subsequent state in response to input (e.g., input to the finite state machine). A transition may define one or more actions or operations that the relational search unit 3700 may implement. One or more of the finite state machines may be non-deterministic, such that the finite state machine may transition from a state to zero or more subsequent states.

The relational search unit 3700 may generate, instantiate, or operate a tokenization finite state machine, which may represent the respective tokenization grammar. Generating, instantiating, or operating a finite state machine may include operating a finite state machine traverser for traversing the finite state machine. Instantiating the tokenization finite state machine may include entering an empty state, indicating the absence of received input. The relational search unit 3700 may initiate or execute an operation, such as an entry operation, corresponding to the empty state in response to entering the empty state. Subsequently, the relational search unit 3700 may receive input data, and the tokenization finite state machine may transition from the empty state to a state corresponding to the received input data. In some embodiments, the relational search unit 3700 may initiate one or more data-queries in response to transitioning to or from a respective state of a finite state machine. In the tokenization finite state machine, a state may represent a possible next token in the request string. The tokenization finite state machine may transition between states based on one or more defined transition weights, which may indicate a probability of transiting from a state to a subsequent state.

The tokenization finite state machine may determine tokenization based on probabilistic path utility. Probabilistic path utility may rank or order multiple candidate traversal paths for traversing the tokenization finite state machine based on the request string. The candidate paths may be ranked or ordered based on one or more defined probabilistic path utility metrics, which may be evaluated in a defined sequence. For example, the tokenization finite state machine may determine probabilistic path utility by evaluating the weights of the respective candidate transition paths, the lengths of the respective candidate transition paths, or a combination thereof. In some implementations, the weights of the respective candidate transition paths may be evaluated with high priority relative to the lengths of the respective candidate transition paths.

In some implementations, one or more transition paths evaluated by the tokenization finite state machine may include a bound state such that the candidate tokens available for tokenization of a portion of the request string may be limited based on the tokenization of a previously tokenized portion of the request string.

Tokenization may include matching a portion of the request string to one or more token types, such as a constant token type, a column name token type, a value token type, a control-word token type, a date value token type, a string value token type, or any other token type defined by the low-latency database analysis system 3000. A constant token type may be a fixed, or invariant, token type, such as a numeric value. A column name token type may correspond with a name of a column in the data model. A value token type may correspond with an indexed data value. A control-word token type may correspond with a defined set of control-words. A date value token type may be similar to a control-word token type and may correspond with a defined set of control-words for describing temporal information. A string value token type may correspond with an unindexed value.

Token matching may include ordering or weighting candidate token matches based on one or more token matching metrics. Token matching metrics may include whether a candidate match is within a defined data scope, such as a defined set of tables, wherein a candidate match outside the defined data scope (out-of-scope) may be ordered or weighted lower than a candidate match within the define data scope (in-scope). Token matching metrics may include whether, or the degree to which, a candidate match increases query complexity, such as by spanning multiple roots, wherein a candidate match that increases complexity may be ordered or weighted lower than a candidate match that does not increase complexity or increases complexity to a lesser extent. Token matching metrics may include whether the candidate match is an exact match or a partial match, wherein a candidate match that is a partial may be ordered or weighted lower than a candidate match that is an exact match. In some implementations, the cardinality of the set of partial matches may be limited to a defined value.

Token matching metrics may include a token score (TokenScore), wherein a candidate match with a relatively low token score may be ordered or weighted lower than a candidate match with a relatively high token score. The token score for a candidate match may be determined based one or more token scoring metrics. The token scoring metrics may include a finite state machine transition weight metric (FSMScore), wherein a weight of transitioning from a current state of the tokenization finite state machine to a state indicating a candidate matching token is the finite state machine transition weight metric. The token scoring metrics may include a cardinality penalty metric (CardinalityScore), wherein a cardinality of values (e.g., unique values) corresponding to the candidate matching token is used as a penalty metric (inverse cardinality), which may reduce the token score. The token scoring metrics may include an index utility metric (IndexScore), wherein a defined utility value, such as one, associated with an object, such as a column wherein the matching token represents the column or a value from the column, is the index utility metric. In some implementations, the defined utility values may be configured, such as in response to user input, on a per object (e.g., per column) basis. The token scoring metrics may include a usage metric (UBRScore). The usage metric may be determined based on a usage based ranking index, one or more usage ranking metrics, or a combination thereof. Determining the usage metric (UBRScore) may include determining a usage boost value (UBRBoost). The token score may be determined based on a defined combination of token scoring metrics. For example, determining the token score may be expressed as the following:

TokenScore=FSMScore*(IndexScore+UBRScore*UBRBoost)+Min(CardinalityScore,1).

Token matching may include grouping candidate token matches by match type, ranking or ordering on a per-match type basis based on token score, and ranking or ordering the match types. For example, the match types may include a first match type for exact matches (having the highest match type priority order), a second match type for prefix matches on ontological data (having a match type priority order lower than the first match type), a third match type for substring matches on ontological data and prefix matches on data values (having a match type priority order lower than the second match type), a fourth match type for substring matches on data values (having a match type priority order lower than the third match type), and a fifth match type for matches omitted from the first through fourth match types (having a match type priority order lower than the fourth match type). Other match types and match type orders may be used.

Tokenization may include ambiguity resolution. Ambiguity resolution may include token ambiguity resolution, join-path ambiguity resolution, or both. In some implementations, ambiguity resolution may cease tokenization in response to the identification of an automatic ambiguity resolution error or failure.

Token ambiguity may correspond with identifying two or more exactly matching candidate matching tokens. Token ambiguity resolution may be based on one or more token ambiguity resolution metrics. The token ambiguity resolution metrics may include using available previously resolved token matching or binding data and token ambiguity may be resolved in favor of available previously resolved token matching or binding data, other relevant tokens resolved from the request string, or both. The token ambiguity resolution may include resolving token ambiguity in favor of integer constants. The token ambiguity resolution may include resolving token ambiguity in favor of control-words, such as for tokens at the end of a request for data, such as last, that are not being edited.

Join-path ambiguity may correspond with identifying matching tokens having two or more candidate join paths. Join-path ambiguity resolution may be based on one or more join-path ambiguity resolution metrics. The join-path ambiguity resolution metrics may include using available previously resolved join-path binding data and join-path ambiguity may be resolved in favor of available previously resolved join-paths. The join-path ambiguity resolution may include favoring join paths that include in-scope objects over join paths that include out-of-scope objects. The join-path ambiguity resolution metrics may include a complexity minimization metric, which may favor a join path that omits or avoids increasing complexity over join paths that increase complexity, such as a join path that may introduce a chasm trap.

The relational search unit 3700 may identify a resolved-request based on the request string. The resolved-request, which may be database and visualization agnostic, may be expressed or communicated as an ordered sequence of tokens representing the request for data indicated by the request string. The relational search unit 3700 may instantiate, or generate, one or more resolved-request objects. For example, the relational search unit 3700 may create or store a resolved-request object corresponding to the resolved-request in the distributed in-memory ontology unit 3500. The relational search unit 3700 may transmit, send, or otherwise make available, the resolved-request to the semantic interface unit 3600.

In some implementations, the relational search unit 3700 may transmit, send, or otherwise make available, one or more resolved-requests, or portions thereof, to the semantic interface unit 3600 in response to finite state machine transitions. For example, the relational search unit 3700 may instantiate a search object in response to a first transition of a finite state machine. The relational search unit 3700 may include a first search object instruction in the search object in response to a second transition of the finite state machine. The relational search unit 3700 may send the search object including the first search object instruction to the semantic interface unit 3600 in response to the second transition of the finite state machine. The relational search unit 3700 may include a second search object instruction in the search object in response to a third transition of the finite state machine. The relational search unit 3700 may send the search object including the search object instruction, or a combination of the first search object instruction and the second search object instruction, to the semantic interface unit 3600 in response to the third transition of the finite state machine. The search object instructions may be represented using any annotation, instruction, text, message, list, pseudo-code, comment, or the like, or any combination thereof that may be converted, transcoded, or translated into structured search instructions for retrieving data from the low-latency data.

The relational search unit 3700 may provide an interface to permit the creation of user-defined syntax. For example, a user may associate a string with one or more tokens. Accordingly, when the string is entered, the pre-associated tokens are returned in lieu of searching for tokens to match the input.

The relational search unit 3700 may include a localization unit (not expressly shown). The localization, globalization, regionalization, or internationalization, unit may obtain source data expressed in accordance with a source expressive-form and may output destination data representing the source data, or a portion thereof, and expressed using a destination expressive-form. The data expressive-forms, such as the source expressive-form and the destination expressive-form, may include regional or customary forms of expression, such as numeric expression, temporal expression, currency expression, alphabets, natural-language elements, measurements, or the like. For example, the source expressive-form may be expressed using a canonical-form, which may include using a natural-language, which may be based on English, and the destination expressive-form may be expressed using a locale-specific form, which may include using another natural-language, which may be a natural-language that differs from the canonical-language. In another example, the destination expressive-form and the source expressive-form may be locale-specific expressive-forms and outputting the destination expressive-form representation of the source expressive-form data may include obtaining a canonical-form representation of the source expressive-form data and obtaining the destination expressive-form representation based on the canonical-form representation. Although, for simplicity and clarity, the grammars described herein, such as the data-analytics grammar and the natural language search grammar, are described with relation to the canonical expressive-form, the implementation of the respective grammars, or portions thereof, described herein may implement locale-specific expressive-forms. For example, the data-analytics tokenizer may include multiple locale-specific data-analytics tokenizers.

The natural language processing unit 3710 may receive input data including a natural language string, such as a natural language string generated in accordance with user input. The natural language string may represent a data request expressed in an unrestricted natural language form, for which data identified or obtained prior to, or in conjunction with, receiving the natural language string by the natural language processing unit 3710 indicating the semantic structure, correlation to the low-latency database analysis system 3000, or both, for at least a portion of the natural language string is unavailable or incomplete. Although not shown separately in FIG. 3 , in some implementations, the natural language string may be generated or determined based on processing an analog signal, or a digital representation thereof, such as an audio stream or recording or a video stream or recording, which may include using speech-to-text conversion.

The natural language processing unit 3710 may analyze, process, or evaluate the natural language string, or a portion thereof, to generate or determine the semantic structure, correlation to the low-latency database analysis system 3000, or both, for at least a portion of the natural language string. For example, the natural language processing unit 3710 may identify one or more words or terms in the natural language string and may correlate the identified words to tokens defined in the low-latency database analysis system 3000. In another example, the natural language processing unit 3710 may identify a semantic structure for the natural language string, or a portion thereof. In another example, the natural language processing unit 3710 may identify a probabilistic intent for the natural language string, or a portion thereof, which may correspond to an operative feature of the low-latency database analysis system 3000, such as retrieving data from the internal data, analyzing data the internal data, or modifying the internal data.

The natural language processing unit 3710 may send, transmit, or otherwise communicate request data indicating the tokens, relationships, semantic data, probabilistic intent, or a combination thereof or one or more portions thereof, identified based on a natural language string to the relational search unit 3700.

The data utility unit 3720 may receive, process, and maintain user-agnostic utility data, such as system configuration data, user-specific utility data, such as utilization data, or both user-agnostic and user-specific utility data. The utility data may indicate whether a data portion, such as a column, a record, an insight, or any other data portion, has high utility or low utility within the system, such as among the users of the system. For example, the utility data may indicate that a defined column is a high-utility column or a low-utility column. The data utility unit 3720 may store the utility data, such as using the low-latency data structure. For example, in response to a user using, or accessing, a data portion, data utility unit 3720 may store utility data indicating the usage, or access, event for the data portion, which may include incrementing a usage event counter associated with the data portion. In some embodiments, the data utility unit 3720 may receive the information indicating the usage, or access, event for the data portion from the insight unit 3730, and the usage, or access, event for the data portion may indicate that the usage is associated with an insight.

The data utility unit 3720 may receive a signal, message, or other communication, indicating a request for utility information. The request for utility information may indicate an object or data portion. The data utility unit 3720 may determine, identify, or obtain utility data associated with the identified object or data portion. The data utility unit 3720 may generate and send utility response data responsive to the request that may indicate the utility data associated with the identified object or data portion.

The data utility unit 3720 may generate, maintain, operate, or a combination thereof, one or more indexes, such as one or more of a usage (or utility) index, a resolved-request index, or a phrase index, based on the low-latency data stored in the distributed in-memory database 3300, the low-latency database analysis system 3000, or both.

The insight unit 3730 may automatically identify one or more insights, which may be data other than data expressly requested by a user, and which may be identified and prioritized, or both, based on probabilistic utility.

The object search unit 3800 may generate, maintain, operate, or a combination thereof, one or more object-indexes, which may be based on the analytical-objects represented in the low-latency database analysis system 3000, or a portion thereof, such as pinboards, answers, and worksheets. An object-index may be a defined data structure, or combination of data structures, for storing analytical-object data in a form optimized for searching. Although shown as a single unit in FIG. 3 , the object search unit 3800 may interface with a distinct, separate, object indexing unit (not expressly shown).

The object search unit 3800 may include an object-index population interface, an object-index search interface, or both. The object-index population interface may obtain and store, load, or populate analytical-object data, or a portion thereof, in the object-indexes. The object-index search interface may efficiently access or retrieve analytical-object data from the object-indexes such as by searching or traversing the object-indexes, or one or more portions thereof. In some implementations, the object-index population interface, or a portion thereof, may be a distinct, independent unit.

The object-index population interface may populate, update, or both the object-indexes, such as periodically, such as in accordance with a defined temporal period, such as thirty minutes. Populating, or updating, the object-indexes may include obtaining object indexing data for indexing the analytical-objects represented in the low-latency database analysis system 3000. For example, the object-index population interface may obtain the analytical-object indexing data, such as from the distributed in-memory ontology unit 3500. Populating, or updating, the object-indexes may include generating or creating an indexing data structure representing an object. The indexing data structure for representing an object may differ from the data structure used for representing the object in other components of the low-latency database analysis system 3000, such as in the distributed in-memory ontology unit 3500.

The object indexing data for an analytical-object may be a subset of the object data for the analytical-object. The object indexing data for an analytical-object may include an object identifier for the analytical-object uniquely identifying the analytical-object in the low-latency database analysis system 3000, or in a defined data-domain within the low-latency database analysis system 3000. The low-latency database analysis system 3000 may uniquely, unambiguously, distinguish an object from other objects based on the object identifier associated with the object. The object indexing data for an analytical-object may include data non-uniquely identifying the object. The low-latency database analysis system 3000 may identify one or more analytical-objects based on the non-uniquely identifying data associated with the respective objects, or one or more portions thereof. In some implementations, an object identifier may be an ordered combination of non-uniquely identifying object data that, as expressed in the ordered combination, is uniquely identifying. The low-latency database analysis system 3000 may enforce the uniqueness of the object identifiers.

Populating, or updating, the object-indexes may include indexing the analytical-object by including or storing the object indexing data in the object-indexes. For example, the object indexing data may include data for an analytical-object, the object-indexes may omit data for the analytical-object, and the object-index population interface may include or store the object indexing data in an object-index. In another example, the object indexing data may include data for an analytical-object, the object-indexes may include data for the analytical-object, and the object-index population interface may update the object indexing data for the analytical-object in the object-indexes in accordance with the object indexing data.

Populating, or updating, the object-indexes may include obtaining object utility data for the analytical-objects represented in the low-latency database analysis system 3000. For example, the object-index population interface may obtain the object utility data, such as from the object utility unit 3810. The object-index population interface may include the object utility data in the object-indexes in association with the corresponding objects.

In some implementations, the object-index population interface may receive, obtain, or otherwise access the object utility data from a distinct, independent, object utility data population unit, which may read, obtain, or otherwise access object utility data from the object utility unit 3810 and may send, transmit, or otherwise provide, the object utility data to the object search unit 3800. The object utility data population unit may send, transmit, or otherwise provide, the object utility data to the object search unit 3800 periodically, such as in accordance with a defined temporal period, such as thirty minutes.

The object-index search interface may receive, access, or otherwise obtain data expressing a usage intent with respect to the low-latency database analysis system 3000, which may represent a request to access data in the low-latency database analysis system 3000, which may represent a request to access one or more analytical-objects represented in the low-latency database analysis system 3000. The object-index search interface may generate one or more object-index queries based on the data expressing the usage intent. The object-index search interface may send, transmit, or otherwise make available the object-index queries to one or more of the object-indexes.

The object-index search interface may receive, obtain, or otherwise access object search results data indicating one or more analytical-objects identified by searching or traversing the object-indexes in accordance with the object-index queries. The object-index search interface may sort or rank the object search results data based on probabilistic utility in accordance with the object utility data for the analytical-objects in the object search results data. In some implementations, the object-index search interface may include one or more object search ranking metrics with the object-index queries and may receive the object search results data sorted or ranked based on probabilistic utility in accordance with the object utility data for the objects in the object search results data and in accordance with the object search ranking metrics.

For example, the data expressing the usage intent may include a user identifier, and the object search results data may include object search results data sorted or ranked based on probabilistic utility for the user. In another example, the data expressing the usage intent may include a user identifier and one or more search terms, and the object search results data may include object search results data sorted or ranked based on probabilistic utility for the user identified by searching or traversing the object-indexes in accordance with the search terms.

The object-index search interface may generate and send, transmit, or otherwise make available the sorted or ranked object search results data to another component of the low-latency database analysis system 3000, such as for further processing and display to the user.

The object utility unit 3810 may receive, process, and maintain user-specific object utility data for objects represented in the low-latency database analysis system 3000. The user-specific object utility data may indicate whether an object has high utility or low utility for the user.

The object utility unit 3810 may store the user-specific object utility data, such as on a per-object basis, a per-activity basis, or both. For example, in response to data indicating an object access activity, such as a user using, viewing, or otherwise accessing, an object, the object utility unit 3810 may store user-specific object utility data indicating the object access activity for the object, which may include incrementing an object access activity counter associated with the object, which may be a user-specific object access activity counter. In another example, in response to data indicating an object storage activity, such as a user storing an object, the object utility unit 3810 may store user-specific object utility data indicating the object storage activity for the object, which may include incrementing a storage activity counter associated with the object, which may be a user-specific object storage activity counter. The user-specific object utility data may include temporal information, such as a temporal location identifier associated with the object activity. Other information associated with the object activity may be included in the object utility data.

The object utility unit 3810 may receive a signal, message, or other communication, indicating a request for object utility information. The request for object utility information may indicate one or more objects, one or more users, one or more activities, temporal information, or a combination thereof. The request for object utility information may indicate a request for object utility data, object utility counter data, or both.

The object utility unit 3810 may determine, identify, or obtain object utility data in accordance with the request for object utility information. The object utility unit 3810 may generate and send object utility response data responsive to the request that may indicate the object utility data, or a portion thereof, in accordance with the request for object utility information.

For example, a request for object utility information may indicate a user, an object, temporal information, such as information indicating a temporal span, and an object activity, such as the object access activity. The request for object utility information may indicate a request for object utility counter data. The object utility unit 3810 may determine, identify, or obtain object utility counter data associated with the user, the object, and the object activity having a temporal location within the temporal span, and the object utility unit 3810 may generate and send object utility response data including the identified object utility counter data.

In some implementations, a request for object utility information may indicate multiple users, or may omit indicating a user, and the object utility unit 3810 may identify user-agnostic object utility data aggregating the user-specific object utility data. In some implementations, a request for object utility information may indicate multiple objects, may omit indicating an object, or may indicate an object type, such as answer, pinboard, or worksheet, and the object utility unit 3810 may identify the object utility data by aggregating the object utility data for multiple objects in accordance with the request. Other object utility aggregations may be used.

The system configuration unit 3820 implement or apply one or more low-latency database analysis system configurations to enable, disable, or configure one or more operative features of the low-latency database analysis system 3000. The system configuration unit 3820 may store data representing or defining the one or more low-latency database analysis system configurations. The system configuration unit 3820 may receive signals or messages indicating input data, such as input data generated via a system access interface, such as a user interface, for accessing or modifying the low-latency database analysis system configurations. The system configuration unit 3820 may generate, modify, delete, or otherwise maintain the low-latency database analysis system configurations, such as in response to the input data. The system configuration unit 3820 may generate or determine output data, and may output the output data, for a system access interface, or a portion or portions thereof, for the low-latency database analysis system configurations, such as for presenting a user interface for the low-latency database analysis system configurations. Although not shown in FIG. 3 , the system configuration unit 3820 may communicate with a repository, such as an external centralized repository, of low-latency database analysis system configurations; the system configuration unit 3820 may receive one or more low-latency database analysis system configurations from the repository, and may control or configure one or more operative features of the low-latency database analysis system 3000 in response to receiving one or more low-latency database analysis system configurations from the repository.

The user customization unit 3830 may receive, process, and maintain user-specific utility data, such as user defined configuration data, user defined preference data, or a combination thereof. The user-specific utility data may indicate whether a data portion, such as a column, a record, autonomous-analysis data, or any other data portion or object, has high utility or low utility to an identified user. For example, the user-specific utility data may indicate that a defined column is a high-utility column or a low-utility column. The user customization unit 3830 may store the user-specific utility data, such as using the low-latency data structure. The user-specific utility data may include, feedback data, such as feedback indicating user input expressly describing or representing the utility of a data portion or object in response to utilization of the data portion or object, such as positive feedback indicating high utility or negative feedback indicating low utility. The user customization unit 3830 may store the feedback in association with a user identifier. The user customization unit 3830 may store the feedback in association with the context in which feedback was obtained. The user customization data, or a portion thereof, may be stored in an in-memory storage unit of the low-latency database analysis system. In some implementations, the user customization data, or a portion thereof, may be stored in the persistent storage unit 3930.

The system access interface unit 3900 may interface with, or communicate with, a system access unit (not shown in FIG. 3 ), which may be a client device, a user device, or another external device or system, or a combination thereof, to provide access to the internal data, features of the low-latency database analysis system 3000, or a combination thereof. For example, the system access interface unit 3900 may receive signals, message, or other communications representing interactions with the internal data, such as data expressing a usage intent and may output response messages, signals, or other communications responsive to the received requests.

The system access interface unit 3900 may generate data for presenting a user interface, or one or more portions thereof, for the low-latency database analysis system 3000. For example, the system access interface unit 3900 may generate instructions for rendering, or otherwise presenting, the user interface, or one or more portions thereof and may transmit, or otherwise make available, the instructions for rendering, or otherwise presenting, the user interface, or one or more portions thereof to the system access unit, for presentation to a user of the system access unit. For example, the system access unit may present the user interface via a web browser or a web application and the instructions may be in the form of HTML, JavaScript, or the like.

In an example, the system access interface unit 3900 may include a data-analytics field user interface element in the user interface. The data-analytics field user interface element may be an unstructured string user input element or field. The system access unit may display the unstructured string user input element. The system access unit may receive input data, such as user input data, corresponding to the unstructured string user input element. The system access unit may transmit, or otherwise make available, the unstructured string user input to the system access interface unit 3900. The user interface may include other user interface elements and the system access unit may transmit, or otherwise make available, other user input data to the system access interface unit 3900.

The system access interface unit 3900 may obtain the user input data, such as the unstructured string, from the system access unit. The system access interface unit 3900 may transmit, or otherwise make available, the user input data to one or more of the other components of the low-latency database analysis system 3000.

In some embodiments, the system access interface unit 3900 may obtain the unstructured string user input as a sequence of individual characters or symbols, and the system access interface unit 3900 may sequentially transmit, or otherwise make available, individual or groups of characters or symbols of the user input data to one or more of the other components of the low-latency database analysis system 3000.

In some embodiments, system access interface unit 3900 may obtain the unstructured string user input may as a sequence of individual characters or symbols, the system access interface unit 3900 may aggregate the sequence of individual characters or symbols, and may sequentially transmit, or otherwise make available, a current aggregation of the received user input data to one or more of the other components of the low-latency database analysis system 3000, in response to receiving respective characters or symbols from the sequence, such as on a per-character or per-symbol basis.

The real-time collaboration unit 3910 may receive signals or messages representing input received in accordance with multiple users, or multiple system access devices, associated with a collaboration context or session, may output data, such as visualizations, generated or determined by the low-latency database analysis system 3000 to multiple users associated with the collaboration context or session, or both. The real-time collaboration unit 3910 may receive signals or messages representing input received in accordance with one or more users indicating a request to establish a collaboration context or session, and may generate, maintain, or modify collaboration data representing the collaboration context or session, such as a collaboration session identifier. The real-time collaboration unit 3910 may receive signals or messages representing input received in accordance with one or more users indicating a request to participate in, or otherwise associate with, a currently active collaboration context or session, and may associate the one or more users with the currently active collaboration context or session. In some implementations, the input, output, or both, of the real-time collaboration unit 3910 may include synchronization data, such as temporal data, that may be used to maintain synchronization, with respect to the collaboration context or session, among the low-latency database analysis system 3000 and one or more system access devices associated with, or otherwise accessing, the collaboration context or session.

The third-party integration unit 3920 may include an electronic communication interface, such as an application programming interface (API), for interfacing or communicating between an external, such as third-party, application or system, and the low-latency database analysis system 3000. For example, the third-party integration unit 3920 may include an electronic communication interface to transfer data between the low-latency database analysis system 3000 and one or more external applications or systems, such as by importing data into the low-latency database analysis system 3000 from the external applications or systems or exporting data from the low-latency database analysis system 3000 to the external applications or systems. For example, the third-party integration unit 3920 may include an electronic communication interface for electronic communication with an external exchange, transfer, load (ETL) system, which may import data into the low-latency database analysis system 3000 from an external data source or may export data from the low-latency database analysis system 3000 to an external data repository. In another example, the third-party integration unit 3920 may include an electronic communication interface for electronic communication with external machine learning analysis software, which may export data from the low-latency database analysis system 3000 to the external machine learning analysis software and may import data into the low-latency database analysis system 3000 from the external machine learning analysis software. The third-party integration unit 3920 may transfer data independent of, or in conjunction with, the system access interface unit 3900, the enterprise data interface unit 3400, or both.

The persistent storage unit 3930 may include an interface for storing data on, accessing data from, or both, one or more persistent data storage devices or systems. For example, the persistent storage unit 3930 may include one or more persistent data storage devices, such as the static memory 1200 shown in FIG. 1 . Although shown as a single unit in FIG. 3 , the persistent storage unit 3930 may include multiple components, such as in a distributed or clustered configuration. The persistent storage unit 3930 may include one or more internal interfaces, such as electronic communication or application programming interfaces, for receiving data from, sending data to, or both other components of the low-latency database analysis system 3000. The persistent storage unit 3930 may include one or more external interfaces, such as electronic communication or application programming interfaces, for receiving data from, sending data to, or both, one or more external systems or devices, such as an external persistent storage system. For example, the persistent storage unit 3930 may include an internal interface for obtaining key-value tuple data from other components of the low-latency database analysis system 3000, an external interface for sending the key-value tuple data to, or storing the key-value tuple data on, an external persistent storage system, an external interface for obtaining, or otherwise accessing, the key-value tuple data from the external persistent storage system, and an internal key-value tuple data for sending, or otherwise making available, the key-value tuple data to other components of the low-latency database analysis system 3000. In another example, the persistent storage unit 3930 may include a first external interface for storing data on, or obtaining data from, a first external persistent storage system, and a second external interface for storing data on, or obtaining data from, a second external persistent storage system.

FIG. 4 is a flowchart of an example of a technique 4000 for validating compacted replay logs. The technique 4000 can be implemented, for example, as a software program that may be executed by a computing device, such as the computing device 1000 of FIG. 1 . The software program can include machine-readable instructions that may be stored in a memory such as the static memory 1200, the low-latency memory 1300, or both of FIG. 1 , and that, when executed by a processor, such the processor 1100 of FIG. 1 , may cause the computing device to perform the technique 4000. The technique 4000 may be implemented by a database system, such as the low-latency database analysis system 3000 shown in FIG. 3 . The technique 4000 may be implemented in whole or in part by one or more units of the database system that may perform replay log compaction, replay log validation, storage management, backup, data loading, database management, data restoration, some other function of the database system, or a combination thereof. In an example, at least one of the enterprise data interface unit 3400 or the distributed cluster manager 3100 of FIG. 3 may implement the technique 4000. The technique 4000 can be implemented using specialized hardware or firmware. Multiple processors, memories, or both, may be used. The distributed database can include a first database instance and a second database instance.

Compacting replay logs results in a compacted replay log. The technique 4000 validates that the replay logs and the compacted replay log resulting therefrom are or are likely to be equivalent in the sense that the replay logs and the compacted replay log result, when replayed, result in the same, or likely the same, data (i.e., the same rows and column values). As, depending on the hashing algorithm used, two different strings may generate the same hash value, there may rare situations where two different resulting rows may have the same hash value and may not be reported (e.g., flagged, identified, etc.) as a non-match. As is known, with some hashing functions, collisions are less likely. The technique 4000 may be performed in response to, and subsequent to, the database system compacting the replay logs. In another example, the technique 4000 may be performed in response to the database system receiving or issuing a command to delete the replay logs.

At 4100, the technique 4000 identifies replay logs to be compacted. The replay logs may be identified in response to a determination that a table is eligible for compaction. In an example, the replay logs may be identified in response to a command (such as of an administrator) to compact data of a table. Other ways of identifying the first replay logs are possible.

In an example, the replay logs may include database manipulation commands for one table of the database. In an example, the replay logs may include database manipulation commands for more than one table of the database. In an example, the replay logs may be or may result from one or more loading events. The replay logs may constitute a version chain of replay logs. In an example, at least one of the replay logs may be or may correspond to a snapshot of low-latency data of one or more tables of the in-memory database. As described above, a snapshot may be stored in the format of a replay log that includes only INSERT commands. As such, the at least one of the replay logs may be a compacted replay log that is the result of a previous successful compaction.

At 4200, the technique 4000 obtains the compacted replay log from the replay logs. As mentioned above, the compacted replay log can include only INSERT commands. INSERT commands can be used to insert new records (e.g., rows) in a table of the database. The INSERT commands of the compacted replay log constitute the non-obsolete, non-redundant, or otherwise non-supplanted commands of the set of commands of the replay logs. Any known technique for obtaining the compacted replay log from the replay logs can be used.

FIG. 5 illustrates an example 5000 of replay logs and a compacted replay log resulting therefrom. The example 5000 includes replay logs 5100 corresponding to four loading events, as illustrated by a loading event indicator 5110. For ease of reference, all the commands of the four replay logs are numbered sequentially, in the order they are processed (e.g., executed, performed, etc.), and as indicated by a command number indicator 5120. For example, even though a command 5140 is the only command of the second loading event, the command 5140 is numbered three (3) to indicate that it is processed later in time than the commands numbered 2 and 1 of the first loading event.

The commands of the example 5000 perform database manipulation commands on the Animals table, which is described above as including the columns ID, NAME, and SOUND. The columns id, name, and sound are illustrated as having the data types integer, string, and string, respectively. However, the disclosure herein is not so limited and a table can include any number of columns that can be of any data types. As the semantics of each of the commands of the replay logs 5100 are understood by a person skilled in the art, descriptions thereof are omitted for brevity.

The example 5000 illustrates a compacted replay log 5200 that results from compacting the replay logs 5100. That is, processing the commands 1-7 of the replay logs 5100, in that order, may be (barring any errors, data corruption, etc.) equivalent to performing only the commands of the compacted replay log 5200. In the replay logs 5100, command number 1, when performed, inserts (i.e., adds) a first row in the Animals table where ID=1, NAME=“CAT,” AND SOUND=“MEOW;” command number 2, when performed, inserts a second row in the Animals table where ID=2, NAME=“DOG,” and SOUND=“BARK;” command number 3, when performed, inserts a third row in the Animals table where ID=1, NAME=“HORSE,” and SOUND=“NEIGH;” command number 4, when performed, deletes any rows in the Animals table where ID=2, which results in the second row being deleted; command number 5, when performed, updates the first row (i.e., where ID=1) to set id to 4, NAME to “COW,” and SOUND to “MOO;” command number 6, when performed, updates all rows where NAME matches the pattern “HOR %” (where % is wildcard indicating: any number of characters) to set the NAME to “LION,” which results in the third row being updated accordingly; and command number 7, when performed, updates all rows where NAME is equal to “LION” to set the ID to the value 5, which results in the third row being updated accordingly. The replay logs 5100 includes only two insert commands and result in two rows being inserted in the Animals table.

Referring to FIG. 4 again, to validate the compacted replay log, the technique 4000 replays, at 4300, the replay logs to obtain a first replay result; replays, at 4400, the compacted replay logs to obtain a second replay result; and compares, at 4500, the first replay result to the second replay result. Replaying, at 4300, the replay logs to obtain the first replay result includes performing blocks 4310-4370. An example of the first replay result and the second replay result are described with respect to FIG. 6 .

At 4310, the technique 4000 identifies condition columns in the replay logs. The technique 4000 can evaluate (e.g., visit, consider, test, etc.) each command of the replay logs to identify conditions columns. A command can include 0 or more condition columns. A column, as used, can mean a column name, a column reference, or any indicator of a column of a table. A condition column of a command is a column (e.g., a column name, a column reference, etc.) that is used in a conditional, a test, or an expression that evaluates to true or false, or is a primary key column of the table. As is known, the primary key or a table may include or may be more than one column of the table. In an example, condition columns can be identified in DELETE commands, UPDATE commands, or data definition commands (e.g., SCHEMA_UPDATE commands), or other commands that may include condition columns. As primary key values can be used to quickly identify rows of the table, primary key columns are included in, and considered to be, condition columns.

It is noted that if the replay logs include data definition commands, such as a schema update command to add or delete a column to a table, the result of the command would be reflected in (e.g., performed on, applied to, etc.) the in-memory database (e.g., the low-latency data). To illustrate, assume that a table T1 includes the column C1, and a first replay log, L1, includes the two commands “Insert into T1 the row (10)” and “Insert into T1 the row (12),” and that a second replay log, L2, includes the schema update command “Add column C2 to T1 with a default value of5.” Thus, the table T1 now includes the rows (10, 5) and (12, 5). As such, when the data of table T1 is exported to obtain the compacted replay log, the compacted replay log would include the insert commands “Insert into T1 the row (C1=10, C2=5)” and “Insert into T1 the row (C1=12, C2=5).”

Referring again to FIG. 5 , condition columns 5150 illustrates the identified condition columns of the replay logs 5100. No condition columns are identified with respect to the commands 1, 2, or 3. Command number 4 includes the condition ID=2, as such the ID column is identified as a condition column. Command number 5 includes the condition ID=1, as such the ID column may be again identified as a condition column. Command 6 includes the condition NAME like “HOR %”, as such the NAME column is a condition column. Command number 7 includes the condition NAME=“LION”, as such the NAME column is again identified as a condition column. Thus, the columns ID and NAME is the set of columns identified as condition columns.

Referring again to FIG. 4 , the technique 4000 now performs blocks 4330-4370 for each command of the replay logs to obtain the first replay result, which consists or data (i.e., rows), or representations of data, that result from performing the commands. To illustrate, the technique 4000 iteratively performs the commands 1-7 of the replay logs 5100. In an example, the first replay result can be stored in a file, in a memory mapped file, in permanent storage, in volatile memory, in the in-memory database, or some other location. Performing a command of a replay log may result in a modification to the first replay result. For example, a row may be added to the first replay result, one or more rows may be delete from the first replay result, one or more column values of one or more rows of the first replay result may be updated, and so on.

At 4320, the technique 4000 determines whether there are more commands in the replay logs to perform. If there are more commands to perform, the technique 4000 proceeds to 4330 (not shown); otherwise, the technique 4000 proceeds to 4400.

At 4330, the technique 4000 determines whether the command is an INSERT command. If the command is an INSERT command, then the technique 4000 prepares a new row to be inserted in the first replay result and proceeds to 4340; otherwise, the technique 4000 proceeds to 4370. At 4340, the technique 4000 stores condition columns of the INSERT command, if any, without any modifications. Stated another way, the column values of the INSERT command corresponding to condition columns identified at 4310 are stored as is (i.e., without modification) in a new row of the first replay result. The values are stored as is because the original values (i.e., unmodified values) will be required when the commands using the condition columns are performed.

At 4350, values of columns that are not condition columns and which are of type string may be stored as modified values in the row to be added to the row to be inserted in the first replay result. The purpose of using the modified value is to store in the row to be inserted a value that requires less storage capacity than the original value but where the modified value is strongly correlated or strongly indicative of the original value. In an example, the modified value can be a hash of the original value. In an example, the original string value can be converted to an MD5 value of a predefined size (e.g., 8 bytes, 16 bytes, etc.) for storage in the row to be inserted. As is known, MD5 is a message-digest algorithm that takes as input a message of arbitrary length and produces as output a 128-bit fingerprint or message digest of the input. Other hashing functions or other functions for obtaining the modified value are possible. In an example, the technique 4000 determines whether the column type is such that the modified value (e.g., the hash value) would require more storage than the original value. In such a case, the column value can be stored as is. To illustrate, a column STATE may be configured to store a maximum of two characters, which requires two bytes to store (i.e., one byte per character). On the other hand, an 8-byte MD5 hash of any value of the STATE column would require more than two bytes. As such, the STATE value can be stored as is.

At 4360, the technique 4000 stores any column values of columns that are of a type other than string as is (i.e., without modification). While not specifically shown in FIG. 4 , the technique 4000 can write (e.g. append, etc.) the row to be inserted in to the first replay result.

According to the foregoing, replaying the first replay logs (i.e., replaying first database manipulation commands of the first replay logs) can include identifying condition columns of the table; responsive to the condition columns not including a column, obtaining a row corresponding to the insert command, where the row includes a modified value of the corresponding value of the column; and adding the row to the first replay result. Condition columns are columns that are used in at least one condition of the first database manipulation commands.

From 4360, the technique 4000 proceeds back to 4320. At 4370, the technique 4000 performs the command, which is not an insert command. As the values of condition columns of the command, if any, are stored as is, the technique 4000 can unambiguously determine which rows of the first replay result to apply the command to.

FIG. 6 illustrates a first replay result 6100 and a second replay result 6200. The first replay result 6100 illustrates the result of replaying, as described with respect to FIG. 4 , the commands of the replay logs 5100. The second replay result 6200 illustrates the result of replaying, as described with respect to FIG. 4 , the commands of the compacted replay log 5200.

The first replay result 6100 illustrates, the running result 6120 of replaying the command indicated by commands 6110. To illustrate, after replaying the command number 1, the first replay result 6100 includes the row ‘1, “CAT”, MD5(“MEOW”)’. As the NAME column is a string column and was identified as a condition column at 4310 of FIG. 4 , the value ‘CAT’ is stored unmodified at 4340 of FIG. 4 ; as the SOUND column is a string column but is not a condition column, a modified value (e.g., MD5(“MEOW”)) of the value of the SOUND is stored at 4350; and the value of the ID column is stored as is at 4360 of FIG. 4 . The same explanation applies with respect to replaying the commands numbered 2 and 3.

As used herein, MD5(<string>) should be understood to be or refer to a result of invoking a function (i.e., an MD5( ) function or the like) with the <string> as a parameter. As such, and to illustrate, the row ‘1, “CAT”, MD5(“MEOW”)’ should in fact be understood to be ‘1, “CAT”, “035466283a3b0c16f87041e2cf843f08”’, where the string “035466283a3b0c16f87041e2cf843f08” is the MD5 of the string “MEOW.” However, for ease of understanding, instead of showing actual hash values (e.g., “035466283a3b0c16f87041e2cf843f08”) in the figures, how the hash values are obtained (e.g., MD5(“MEOW”)) are shown.

After command number 3 is replayed, the first replay result 6100 includes the three rows indicated by rows 6130. Command number 4 deletes any rows where the value of ID is equal to 2. Thus, after replaying the command number 4, the first replay result 6100 includes the two rows indicated by rows 6140. Command number 5 essentially replaces the row where ID=1 with the row (4, “COW”, MD5(“MOO”)). When command number 7 is finally replayed, the first replay result 6100 includes only the rows indicated by rows 6150.

In an implementation, after all commands of the first replay log are replayed, the technique 4000 can hash each of the obtained rows to further reduce the memory required to store the first replay results. A result 6160 illustrates the first replay result 6100 where each row is hashed. To obtain a hash of a row, each non-string value can be converted into a respective string, the values are then concatenated according to an ordering of the columns, and a hash of the concatenated string is obtained. A hashed row 6162 illustrates obtaining a hash of a row 6164 (i.e., the row ‘4, “COW”, MD5(“MOO”)’). The value of the non-string ID column (e.g., the value 4) is converted into a string (as illustrated by the function STRING(4), which provides the string “4” of the integer value 4); the plus (i.e., +) operation illustrates the string concatenation operation. As such, the strings “4,” “COW,” and MD5(“MOO”) are concatenated to obtain the string “4COWae176556a77ece20119ee093f1c0ebbb,” where “ae176556a77ece20119ee093f1c0ebbb” is the MD5 hash of “MOO.” The whole concatenated string is then hashed to obtain the hashed row value.

In an example, a separator can be inserted between the values of the row before hashing. Using “_” as a separator, the hash value can be obtained as MD5(STRING(4)+“_”+“COW”+“_”+MD5(“MOO”)). Other separators are possible. In another example, each string that is not already hashed can be hashed, the hashed strings can be concatenated, and a hashed row can be obtained for the concatenated string. To illustrate, the hashed row can be obtained as MD5(MD5(STRING(4)+MD5(“COW”)+MD5(“MOO)). Other ways of obtaining the hashed row value are possible.

Referring to FIG. 4 again, at 4400, the technique 4000 replays the compacted replay log to obtain a second replay result. As such, the technique 4000 replays the compacted replay log 5200 of FIG. 5 to obtain the second replay result 6200 of FIG. 6 . The technique 4000 hashes the values and the rows of the second replay result 6200 in such way that the column values and rows are hashed similarly to the rows of the first replay result 6100; otherwise, the rows of the first replay result 6100 and the second replay result 6200 could not be compared. It is noted that the order of the rows in the first replay result matches the order of the rows in the second replay result. At least in the case that there are no errors, the second replay results would have been created in the order specified in (i.e., by applying the commands of) the first replay logs in the order specified in the first replay logs. That is, for example, row number N of the first replay result corresponds to (i.e., should be the same as, etc.) row number N of the second replay result. In an example, the technique 4000 hashes the values of only those columns identified as condition columns at 4310. A result 6210 illustrates hashing the rows of the second replay result 6200 similarly to the hashing of the first replay result 6100 to obtain the result 6160. In an example, the value of each string column is hashed, as described with respect to the first replay result 6100. In an example, the technique 4000 uses separators between the column values (whether hashed or not, as the case may be).

At 4500, the technique 4000 compares the first replay result to the second replay result. In an example, the technique 4000 compares each hashed row value of the first replay result to a corresponding hashed row value of the second replay result. The technique 4000 can write the row numbers of non-matching rows, such as to a log file.

In an example, in response to a first identified non-match, the technique 4000 can send a notification of the non-match, such as to an administrator (e.g., a database administrator) and stop the verification. As such, in an example, the technique 4000 may not replay all of the commands of the compacted replay log 5200 to obtain the second replay result 6200 before commencing the comparing at 4500. Rather, the technique 4000 can perform one command of the compacted replay log 5200 to obtain one corresponding hashed row of the second replay result 6200. The technique 4000 can then compare the row of the second replay result 6200 (i.e., the hashed row value of the row of the second replay result 6200) to the corresponding row of first replay result 6100 (i.e., the hashed row value of the corresponding row of first replay result 6100). In case of a match, the technique 4000 discards the row of the second replay result 6200 and proceeds to perform a next command of the compacted replay log 5200 to obtain a next row of the second replay result 6200 for comparison with corresponding row of first replay result 6100; and so on. In case of a non-match, the technique 4000 can stop the verification and transmit a notification of the non-match. As such, the technique 4000 does not consume memory resources to hold the second replay result 6200 as the technique 4000 only generates one row of the second replay result 6200 as a time.

FIG. 7 is a flowchart of an example of a technique 7000 for database replay log verification. The technique 700 determines whether a first replay log of first commands for obtaining a first in-memory database, and a second replay log of second commands for obtaining a second in-memory database are equivalent. That the first replay log and the second replay log are equivalent can mean that the first commands and the second commands, when replayed (e.g., executed, performed, etc.) are or are likely to produce the same in-memory database (i.e., the same data). The first replay log may include one or more first replay logs. The first replay log can be as described above. In an example, the second replay log can be a compacted version of the first replay log that has been generated using a compaction process, as described above. However, that need not be the case, the technique 7000 can be used to compare any two sets of replay logs.

The technique 7000 can be implemented, for example, as a software program that may be executed by a computing device, such as the computing device 1000 of FIG. 1 . The software program can include machine-readable instructions that may be stored in a memory such as the static memory 1200, the low-latency memory 1300, or both of FIG. 1 , and that, when executed by a processor, such the processor 1100 of FIG. 1 , may cause the computing device to perform the technique 7000. The technique 7000 may be implemented by a database system, such as the low-latency database analysis system 3000 shown in FIG. 3 . The technique 7000 may be implemented in whole or in part by one or more units of the database system that may perform replay log compaction, replay log validation, storage management, backup, data loading, database management, data restoration, some other function of the database system, or a combination thereof. In an example, at least one of the enterprise data interface unit 3400 or the distributed cluster manager 3100 of FIG. 3 may implement the technique 7000. The technique 7000 can be implemented using specialized hardware or firmware. Multiple processors, memories, or both, may be used. The distributed database can include a first database instance and a second database instance.

At 7100, the technique 7000 replays the first replay log to generate a first replay result. As described above, the first replay log includes commands for obtaining an in-memory database. The first replay log can be replayed as described with respect to FIG. 4 . As described above, replaying the first replay log can include replacing 7150 a first value of a first field (e.g., column, etc.) included in a first command in the first replay log with a first hash value responsive to a determination that the first field is not utilized as a condition in at least one command included in the first replay log.

At 7200, the technique 7000 replays a second replay log to generate a second replay result. Replaying the second replay log can include replacing a value of a field included in a command of the second replay log with a hash value. In a case that the replay log is a compacted version of the first replay log that includes only INSERT commands, replaying the second replay log can be as described with respect to replaying the second replay log of FIG. 3 . In an example, the second replay log may not be a compacted version of the first replay log. As such, the second replay log may include commands other than (e.g., in addition to, etc.) INSERT commands and replaying the second replay log can be as described with respect to replaying the first replay log.

In an example and in a case where the second replay log is a compacted version of the first replay log, replacing the second value of the second field included in the second command of the second replay log with the second hash value can include replacing the second value of the second field included in the second command of the second replay log with the second hash value responsive to a determination that the second field is not utilized as a condition in commands included in the first replay log. In an example, and in a case where the second replay log includes commands other than INSERT commands, replacing the second value of the second field included in the second command of the second replay log with the second hash value can include replacing the second value of the second field included in the second command of the second replay log with the second hash value responsive to a determination that the second field is not utilized as a condition in commands included in the second replay log.

At 7300, the technique 7000 compares the first replay result and the second replay result to verify that the first replay log and the second replay log are equivalent. Comparing the first replay result and the second replay result to verify the compaction of the second replay log can include comparing a third hash value of a row of the first replay result (i.e., a hashed value of the entire row of the first replay result) to a fourth hash value of a corresponding row of the second replay result (i.e., a hashed value of the entire row of the first replay result). Obtaining the hashed value of a row can be as described with respect to FIG. 4 .

Generating the second replay result at 7200 encompasses generating all the rows of the second replay result before the comparing at 7300 and encompasses generating one row of the second replay result at a time and using the row in the comparing at 7300 before generating a next row. In an example, responsive to a non-match between the third hash value and the fourth hash value, the technique 7000 transmits a notification of the non-match and stops the verification. In an example, transmitting the notification of the non-match can include logging the non-match to a log file.

FIG. 8 is a flowchart of an example of a technique 8000 for database replay log compaction verification. The technique 8000 can be implemented, for example, as a software program that may be executed by a computing device, such as the computing device 1000 of FIG. 1 . The software program can include machine-readable instructions that may be stored in a memory such as the static memory 1200, the low-latency memory 1300, or both of FIG. 1 , and that, when executed by a processor, such the processor 1100 of FIG. 1 , may cause the computing device to perform the technique 8000. The technique 8000 may be implemented by a database system, such as the low-latency database analysis system 3000 shown in FIG. 3 . The technique 8000 may be implemented in whole or in part by one or more units of the database system that may perform replay log compaction, replay log validation, storage management, backup, data loading, database management, data restoration, some other function of the database system, or a combination thereof. In an example, at least one of the enterprise data interface unit 3400 or the distributed cluster manager 3100 of FIG. 3 may implement the technique 8000. The technique 8000 can be implemented using specialized hardware or firmware. Multiple processors, memories, or both, may be used. The distributed database can include a first database instance and a second database instance.

At 8100, the technique 8000 replays, to obtain a first replay result, first database manipulation commands of at least one replay log, where the first database manipulation commands comprises at least one of an UPDATE command or a DELETE command. At 8200, the technique 8000 replays, to obtain a second replay result, second database manipulation commands of a compacted replay log, where the compacted replay log includes only INSERT commands. The compacted replay log can be a compacted version of the first replay log that has been generated using a compaction process. At 8300, responsive to a row of the first replay result not matching a corresponding row of the second replay result, the technique 8000 sends a notification indicating a non-match corresponding to the row of the first replay result.

In an example, responsive to the first replay result matching the second replay result, the technique 8000 deletes the at least one replay log. In an example, and as described above, replaying, to obtain a first replay result, first database manipulation commands of at least one replay log can include identifying condition columns of the first database manipulation commands; and replacing a first value of a field included in a first command in the first database manipulation commands with a hash value responsive to a determination that the field is not included in the condition columns. In an example, the technique 8000 can include obtaining a first hashed row value for the row of the first replay result and obtaining a second hashed row value for corresponding row of the second replay result. Obtaining the first hashed row value and the second hashed row value can be as described above. In an example, the technique 8000 compares the first hashed row value and the second hashed row value to determine whether the row of the first replay result matches the corresponding row of the second replay result.

As mentioned, validating a compacted replay log of a table can mean, encompass, or include validating a compacted replay log of a shard of the table. In a distributed database, such as a distributed in-memory database as described herein, a table may be partitioned into shards. The data of the sharded table can be low-latency data as described herein. Sharding a table includes distributing the data (e.g., rows) of the sharded table amongst the shards in such a way that a row of the sharded table is included in a shard and is omitted from the other shards. Sharding a table may include distributing the rows of the table amongst the shards according to sharding criteria. Sharding a table may include distributing the rows of the table to respective shards based on the value in the row for a column identified by the sharding criteria. For examples, rows of a sharded table having a first value for the column identified by the sharding criteria may be included in a first shard and omitted from a second shard and rows of the sharded table having a second value for the column may be included in the second shard and omitted from the first shard.

The sharding criteria can be derived from one or more columns of the table. The sharding criteria can be derived from one column of the sharded table, can use more than one column of the table, or can be some other criteria. The sharding criteria may be used to distribute rows of the table amongst the available shards. This may include arranging the distribution of the rows in a manner such that rows with the same sharding criteria value(s) are placed in the same shard where feasible based on the number of shards and the variation in the number of rows per shard that is desired. In some implementations, all the rows of the table that have the same sharding criteria value(s) may be stored in only one of the shards. A shard can include more than one value of the sharding criteria.

A table can be sharded into tens, hundreds, or more shards. A shard can include, for example, zero rows or millions of rows of data. The shards can be distributed to database instances of the distributed database, such as the in-memory database instances described herein. In an example, the number of shards can be a multiple of the number of database instances. As such, more than one shard can be distributed to a database instance. The database instances may be implemented on various different computing devices. Some database instances may be implemented on the same computing device.

As used herein, the terminology “computer” or “computing device” includes any unit, or combination of units, capable of performing any method, or any portion or portions thereof, disclosed herein.

As used herein, the terminology “processor” indicates one or more processors, such as one or more special purpose processors, one or more digital signal processors, one or more microprocessors, one or more controllers, one or more microcontrollers, one or more application processors, one or more central processing units (CPU)s, one or more graphics processing units (GPU)s, one or more digital signal processors (DSP)s, one or more application specific integrated circuits (ASIC)s, one or more application specific standard products, one or more field programmable gate arrays, any other type or combination of integrated circuits, one or more state machines, or any combination thereof.

As used herein, the terminology “memory” indicates any computer-usable or computer-readable medium or device that can tangibly contain, store, communicate, or transport any signal or information that may be used by or in connection with any processor. For example, a memory may be one or more read only memories (ROM), one or more random access memories (RAM), one or more registers, low power double data rate (LPDDR) memories, one or more cache memories, one or more semiconductor memory devices, one or more magnetic media, one or more optical media, one or more magneto-optical media, or any combination thereof.

As used herein, the terminology “instructions” may include directions or expressions for performing any method, or any portion or portions thereof, disclosed herein, and may be realized in hardware, software, or any combination thereof. For example, instructions may be implemented as information, such as a computer program, stored in memory that may be executed by a processor to perform any of the respective methods, algorithms, aspects, or combinations thereof, as described herein. Instructions, or a portion thereof, may be implemented as a special purpose processor, or circuitry, that may include specialized hardware for carrying out any of the methods, algorithms, aspects, or combinations thereof, as described herein. In some implementations, portions of the instructions may be distributed across multiple processors on a single device, on multiple devices, which may communicate directly or across a network such as a local area network, a wide area network, the Internet, or a combination thereof.

As used herein, the terminology “determine,” “identify,” “obtain,” and “form” or any variations thereof, includes selecting, ascertaining, computing, looking up, receiving, determining, establishing, obtaining, or otherwise identifying or determining in any manner whatsoever using one or more of the devices and methods shown and described herein.

As used herein, the term “computing device” includes any unit, or combination of units, capable of performing any method, or any portion or portions thereof, disclosed herein.

As used herein, the terminology “example,” “embodiment,” “implementation,” “aspect,” “feature,” or “element” indicates serving as an example, instance, or illustration. Unless expressly indicated, any example, embodiment, implementation, aspect, feature, or element is independent of each other example, embodiment, implementation, aspect, feature, or element and may be used in combination with any other example, embodiment, implementation, aspect, feature, or element.

As used herein, the terminology “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from context, “X includes A or B” is intended to indicate any of the natural inclusive permutations. That is, if X includes A; X includes B; or X includes both A and B, then “X includes A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from the context to be directed to a singular form.

Further, for simplicity of explanation, although the figures and descriptions herein may include sequences or series of steps or stages, elements of the methods disclosed herein may occur in various orders or concurrently. Additionally, elements of the methods disclosed herein may occur with other elements not explicitly presented and described herein. Furthermore, not all elements of the methods described herein may be required to implement a method in accordance with this disclosure. Although aspects, features, and elements are described herein in particular combinations, each aspect, feature, or element may be used independently or in various combinations with or without other aspects, features, and elements.

Although some embodiments herein refer to methods, it will be appreciated by one skilled in the art that they may also be embodied as a system or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “processor,” “device,” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable mediums having computer readable program code embodied thereon. Any combination of one or more computer readable mediums may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to CDs, DVDs, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

Attributes may comprise any data characteristic, category, content, etc. that in one example may be non-quantifiable or non-numeric. Measures may comprise quantifiable numeric values such as sizes, amounts, degrees, etc. For example, a first column containing the names of states may be considered an attribute column and a second column containing the numbers of orders received for the different states may be considered a measure column.

Aspects of the present embodiments are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a computer, such as a special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

While the disclosure has been described in connection with certain embodiments, it is to be understood that the disclosure is not to be limited to the disclosed embodiments but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims, which scope is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures as is permitted under the law. 

1. A method for database replay log compaction verification, comprising: identifying at least one replay log of a table, the at least one replay log comprising first database manipulation commands; obtaining a compacted replay log of the table, the compacted replay log comprising second database manipulation commands, wherein the second database manipulation commands are insert commands, and wherein an insert command of the first database manipulation commands includes a column and a corresponding value for the column; replaying, to obtain a first replay result, the first database manipulation commands, wherein replaying the first database manipulation commands comprises: identifying condition columns of the table, wherein the condition columns are used in at least one condition of the first database manipulation commands; obtaining a row corresponding to the insert command, wherein obtaining the row corresponding to the insert command comprises: including in the row one of a modified value of the corresponding value of the column or the corresponding value based on whether the condition columns include the column of the insert command; adding the row to the first replay result; replaying, to obtain a second replay result, the second database manipulation commands, wherein replaying the second database manipulation commands comprises: determining whether to include in rows of the second replay result modified values of values of columns based on the condition columns; and responsive to one row of the first replay result not matching a corresponding row of the second replay result, sending a notification including a non-match.
 2. The method of claim 1, wherein the column is of type string.
 3. The method of claim 2, wherein the modified value is a hash of the corresponding value.
 4. The method of claim 1, wherein the column is a first column and the corresponding value is a first value, wherein the insert command further includes a second column and a second corresponding value for the second column, further comprising: responsive to the second column being of a type other than string, including the second corresponding value in the row.
 5. The method of claim 1, wherein adding the row to the first replay result comprises: adding a hash value of the row to the first replay result.
 6. The method of claim 1, further comprising: comparing a first hash value of the one row of the first replay result to a second hash value of the corresponding row of the second replay result to determine whether the one row of the first replay result matches the corresponding row of the second replay result.
 7. The method of claim 1, wherein replaying, to obtain a second replay result, the second database manipulation commands comprises: obtaining the corresponding row of the second replay result; and responsive to the one row matching the corresponding row of the second replay result, obtaining another row of second replay result for comparing to a corresponding row of the first replay result. 8-14. (canceled)
 15. The method of claim 1, wherein the compacted replay log is a compacted version of the at least one replay log that has been generated using a compaction process.
 16. A method for replay log compaction verification, comprising: replaying, to obtain a first replay result, first database manipulation commands of at least one replay log, wherein the first database manipulation commands comprises at least one of an update command or a delete command; replaying, to obtain a second replay result, second database manipulation commands of a compacted replay log, wherein the second database manipulation commands are insert commands, and wherein the compacted replay log is a compacted version of the first replay log that has been generated using a compaction process; and responsive to a row of the first replay result not matching a corresponding row of the second replay result, sending a notification indicating a non-match corresponding to the row of the first replay result.
 17. The method of claim 16, further comprising: responsive to the first replay result matching the second replay result, deleting the at least one replay log.
 18. The method of claim 16, wherein replaying, to obtain a first replay result, the first database manipulation commands of at least one replay log comprises: identifying condition columns of the first database manipulation commands; and replacing a first value of a field included in a first command in the first database manipulation commands with a hash value responsive to a determination that the field is not included in the condition columns.
 19. The method of claim 16, further comprising: obtaining a first hashed row value for the row of the first replay result; and obtaining a second hashed row value for the corresponding row of the second replay result.
 20. The method of claim 19, further comprising: comparing the first hashed row value and the second hashed row value to determine whether the row of the first replay result matches the corresponding row of the second replay result.
 21. A device, comprising: a memory; and a processor, the processor configured to execute instructions stored in the memory for database replay log compaction verification, the instructions comprise instructions to: identify at least one replay log of a table, the at least one replay log comprising first database manipulation commands; obtain a compacted replay log of the table, the compacted replay log comprising second database manipulation commands, wherein the second database manipulation commands are insert commands, and wherein an insert command of the first database manipulation commands includes a column and a corresponding value for the column; replay, to obtain a first replay result, the first database manipulation commands, wherein to replay the first database manipulation commands comprises to: identify condition columns of the table, wherein the condition columns are used in at least one condition of the first database manipulation commands; obtaining a row corresponding to the insert command, wherein obtaining the row corresponding to the insert command comprises: responsive to the condition columns not including the column of the insert command, including a modified value of the corresponding value of the column in the row; and responsive to the condition columns including the column of the insert command, including the corresponding value of the column in the row; add the row to the first replay result; replay, to obtain a second replay result, the second database manipulation commands; and responsive to one row of the first replay result not matching a corresponding row of the second replay result, send a notification including a non-match.
 22. The device of claim 21, wherein the column is of type string.
 23. The device of claim 22, wherein the modified value is a hash of the corresponding value.
 24. The device of claim 21, wherein the column is a first column and the corresponding value is a first value, wherein the insert command further includes a second column and a second corresponding value for the second column, and wherein the processor is further configured to execute instructions stored in the memory to: responsive to the second column being of a type other than string, include the second corresponding value in the row.
 25. The device of claim 21, wherein to add the row to the first replay result comprises to: add a hash value of the row to the first replay result.
 26. The device of claim 21, wherein the processor is further configured to execute instructions stored in the memory to: compare a first hash value of the one row of the first replay result to a second hash value of the corresponding row of the second replay result to determine whether the one row of the first replay result matches the corresponding row of the second replay result.
 27. The device of claim 21, wherein to obtain the second replay result, the second database manipulation commands comprises to: obtain the corresponding row of the second replay result; and responsive to the one row matching the corresponding row of the second replay result, obtain another row of second replay result for comparing to a corresponding row of the first replay result. 